Web Fraud Prevention, Identity Verification & Authentication Guide 2018 2019 Prevention


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Web Fraud Prevention, Identity Verication
& Authentication Guide 2018 -2019
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Web Fraud Prevention, Identity Verication
& Authentication Guide 2018 -2019
Editor’s letter
Customer experience and the conict between oering a fric
tionless customer service to good clients while managing risk
and blocking the bad guys are some themes that are emerging
from acquirers, card schemes, regulators, service providers,
merchants, as well as auditors and journalists alike.
Identifying fraudulent behaviour without rejecting or oending
good customers is key because a blocked good customer will
not return, and as the market is so competitive, they can go every-
where. Moreover, automation technologies based on machine
learning and articial intelligence are gaining prominence in this
conversation. But, as always, some challenges in addressing these
themes, security-wise, still remain.
The Web Fraud Prevention, Identity Verification &
Authentication Guide 2018-2019
To respond to some of these challenges, we have released our
7th edition of the Web Fraud Prevention, Identity Verication
& Authentication Guide to provide payment and fraud and risk
management professionals with a series of insightful perspectives
from industry associations and leading market players on key
aspects of the global digital identity, transactional and web fraud
detection space.
The guide is structured in three parts; the rst part focuses
on presenting the industry, with its most acute problems, but
also shares some best practices from industry leading players on
how to tackle them. With the advent of digitalisation and the use
of smartphones, business and fraud coexist globally, both seen
as profitable activities, involving large masses of customers.
The surge in demand for many goods and services has enabled
not only businesses’ profits to soar but also fraudsters to
capitalize on this growth. Bad actors are tricking retailers/
merchants/banks by hiding beneath large transaction volumes
and exploiting the fact that many products and services providers
are willing to accept a greater degree of risk in order to approve
more orders.
Key challenges for businesses
One of the biggest challenges in the fraud detection space for
retailers/merchants is that for consumers, a transaction needs
to happen in the blink of an eye, and therefore fraud controls
should be invisible for them.
However, fraud attacks are becoming more sophisticated, with
fraudsters having access to the latest technology and sophis ti cated
tools. Therefore, what is really needed? A fraud management
solution can track the customer’s behavioural patterns (beha-
vioural profiling) and instantly detect and report any signs
of fraud, triggering a step up authentication to mitigate the
potential risk (risk-based authentication).
Similarly, when it comes to financial institutions (FIs), FIs
are under intense competitive pressure to make the banking
experience easier and frictionless (while regulators in Europe
appear to be taking the industry in a dierent direction, thanks to
the second Payment Services Directive’s requirement for Strong
Customer Authentication).
The faceless nature of the online and mobile channels makes
authentication hard, however the large amounts of data that have
been breached in recent years combined with fraudsters’ use of
phishing, social engineering, and malware make authentication
much more dicult. As a result, some of the top threats for 2018
in ecommerce and banking are account takeover and new
account applications, according to Aite.
For Europe especially, but also for the US, Canada and Australia,
in 2018, financial discussions revolved around Open Banking
initiatives. The concept of open banking promises users greater
control over their nancial data; however, it is not without risks,
and its success is tied to consumer condence when it comes
to the security and privacy of their information.
At the moment, businesses have become incredibly dependent
on a network of systems to manage, store, and transmit in for
mation such as nancial accounts, personally identiable informa
tion, intellectual property, transaction records etc. Within this web,
authentication, validation and verication have turned out to be
central to the ability of these businesses to effectively secure
access to consumer-facing digital channels and the systems that
underpin their operations.
The right tools for ghting fraud
The second part of our Web Fraud Prevention, Identity Veri
cation & Authentication Guide 20182019 focuses on mapping
the key players in the fraud detection, identity verification
and online authentication space. The chapter aims to create
an accurate picture of what the fraud detection, identity
verication and online authentication oerings looks like, and
it displays the key players of the industry together with their
main capabilities. Depicting the most important features of each
company is part of our goal of helping merchants, banks, ntechs
and payment service providers to grasp the current market
opportunities and to use them according to their own needs.
The whole range of capabilities is designed to address the pain
points that organizations in the payments space are struggling
to remove. To do so, security and risk management leaders
invol ved in online fraud detection have started using machine
lear ning analytics, cloudbased deployment options, articial
intelligence, behavioural analytics, and massive global data
Such technologies generate real-time insights into the nuanced
patterns of fraud to enable businesses to spot and ght fraud.
These patterns are based on geography, industry, time of day,
time of year, and over 15,000 other signals. Fraud management
specialists/vendors have developed networks that analyse
millions of transactions in real time across billions of devices.
Finally, the third part of our Web Fraud Prevention guide, the
Company Proles section, oers insights into the capabilities
fraud prevention companies offer businesses in order to spot
fraudulent attacks, stop them and prevent them from happening.
Obviously, we would like to express our appreciation to the
Merchant Risk Council and Holland FinTech – our endorsement
partners who have constantly supported us – and also to our
thought leaders, participating organisations and top industry
players that contributed to this edition, enriching it with valuable
insights and, thus, joining us in our constant endeavour to depict
an insightful picture of the industry.
Businesses may think they understand fraud, but the reality
is far more complex, and this lack of insight could lead to
guessing, incorrect conclusions, and bad decisions. Premises
such as the fraudsters as geeky guys, conducting their activi
ties at night in their basements, and living somewhere in
Eastern Europe, or that ATOs are relatively low prole events
could shape businesses’ fraud-fighting operations from top to
bottom. Moreover, these assumptions help determine how ana-
lysts set up rules, how many people the fraud team hires and
stas on a given day, and so on.
Therefore, security and risk management leaders responsible for
fraud prevention and payment security should align with cross
organisational groups (security, identity and access mana ge ment,
credit/underwriting) to detect highrisk or anomalous activity
and identity, and tap into technologies that enable fighting
against these threats. And if we consider the large amounts of
har vested data, the capability of analysing and connecting
data across channels is vital for strong defence.
Enjoy your reading!
Mirela Ciobanu
Senior Editor, The Paypers
Table of contents
Editor’s Letter: The Complex Faces of Risk Management and Fraud
1 Fraud Management – Trends and Developments
1.1 Overview on the Innovation Taking Place in the Fraud Management Space – Machine Learning and
Articial Intelligence
The Rise of Machine Learning/Articial intelligence in Fraud Detection – Introduction to ML&AI in Fraud Management |
Mirela Ciobanu, Senior Editor, The Paypers
Machine Learning Against Online Fraud: The Advantage of a Risk-Based Approach | Ralf Gladis, Co-Founder and CEO,
Why Implement a Fraud Management Solution that Combines Machine Learning with Rules? | Mark W. Hall, Sr. Director
Global Solutions Marketing, Fraud Management, CyberSource
Brick and Mortar Navigates Digital Transformation | Don Bush, Vice President of Marketing, Kount
Why a Machine Learning Based Approach to Mitigate This Risk Is Key in Fraud Prevention | Pavel Gnatenko,
Risk management expert, Covery
1.2 Best Practices in the Fraud Management Space
Collaboration Paving the Way for Ecommerce Customer Experience | Keith Briscoe, Chief Marketing Ocer, Ethoca
Interview with RISK IDENT on the Challenges Merchants Face on Both Sides of the Atlantic | Felix Eckhardt,
Managing Director and CTO, Piet Mahler, COO, RISK IDENT
Are You Ready for the New Era of Online Payments? | Amador Testa, Chief Product Ocer, Emailage
Account Takeover via Hacking Bots (The Rise of the Bots) | Neira Jones, Advisor and Ambassador, Emerging Payments
Interview with MRC on the Way This Community Evolved to Support Merchants in Fighting Payments and Commerce
Fraud | Paul Kuykendal, CEO, Merchant Risk Council
1.3 Best Practices of Mitigating Fraud in Ecommerce - the State of Aairs in Ecommerce Verticals
Fraud in Ecommerce – Diagnosis and Treatment | Mirela Ciobanu, Senior Editor, The Paypers
Interview with Sift Science on Preventing Loyalty Fraud in Travelling | Kevin Lee, Trust and Safety Architect, Sift Science
Fraud in Airline Travel Industry – Airlines Need Better Anti-Fraud Data | Ronald Praetsch, Co-Founder and Managing
Director, about-fraud.com
Telecoms Fraud – The Impact of Digitalisation | Jason Lane-Sellers, President and Director, CFCA
Sim Swap Fraud – an Attack in Multiple Stages | Emma Mohan-Satta, Senior Fraud Manager, Capital on Tap
Interview with Ubisoft on the Status of Online Gaming Industry Fraud, with Insights into the Grey Market |
Sithy Phoutchanthavongsa, Fraud Expert, Ubisoft
With Low Order Volumes, Richemont Faces a Dierent Fraud Review Challenge | Leon Brown, Fraud and Payments
Manager, Richemont
Table of contents
1.4 Best Practices of Mitigating Fraud in Banking
Fraud Mitigation – Key Challenges for Banks | Mirela Ciobanu, Senior Editor, The Paypers
Machine Learning Innovations for Fighting Financial Crime in an Open Banking Era | Pedro Bizarro, Chief Science
Ocer, Feedzai
Accertify and InAuth: Fighting Fraudulent Account Opening | Michael Lynch, Chief Strategy Ocer, InAuth
Interview with Nordea on Cybercrime Trends and Fraud Management Solutions | Fraud Awareness and Communication
team of Nordea
2 Online Authentication – The Journey from Passwords and Secret Questions to
Zero Factor Authentication
An introduction to Online Authentication and Stronger Authentication | Mirela Ciobanu, Senior Editor, The Paypers
Reimagining Identity in the Post-Data Breach Era | Alisdair Faulkner, Chief Identity Officer, Business Services, ThreatMetrix,
a LexisNexis Risk Solutions company
Adaptive Authentication: Balance Opportunity and Risk in an Omnichannel World | Mathew Long, Senior Advisor,
Fraud & Risk Intelligence, RSA
Interview with HID Global on the Role Adaptive Authentication Plays within the Open Banking Ecosystem |
Olivier Thirion de Briel, Global Solution Marketing Director, HID Global
Seamless and Secure Online Authentication: A Solvable Goal? | Robert Holm, Senior Vice President Fraud Management,
Arvato Financial Solutions
Account Takeover and Step Up Authentication – True Customer Satisfaction Means Optimizing Experiences and
Relationships from Start to Finish | Andrew Gowasack, Cofounder and Managing Director, Trust Stamp
Interview with CA Technologies on PSD2, 3DS 2.0, and the New Authentication Landscape | James Rendell, Payment
Security Strategy and Product Management, CA Technologies
Complex Fraud Threats Call for Adaptive Detection Tools | Rahul Pangam, Co-Founder and CEO, Simility, a PayPal
The Journey towards Zero Factor Authentication | Yinglian Xie, CEO and Co-founder, DataVisor
2019: The Push for Orchestrated Authentication | Julie Conroy, Research Director, Aite Group
Open Banking: Why a New Approach to Authentication Is Key to its Success | Brett McDowell, FIDO Alliance
3 Customer Onboarding and Digital Identity Verication
3.1 Customer Onboarding and Identity Verication
An introduction to Customer Onboarding and Digital Identity Verication | Mirela Ciobanu, Senior Editor, The Paypers
Interview with Melissa on Best Practices in KYC | Barley Laing, Managing Director, Melissa Global Intelligence
Hard Problems: Identity Verication, Fraud Prevention and the Giant Leap Towards Financial Inclusion | Zac Cohen,
General Manager, Trulioo
Digitising Complex Onboarding Processes: Who Will Be Leading in Getting It Right? | Josje Fiolet, Manager, Lead Digital
Onboarding, INNOPAY
Interview with Steve Cook on Latest Trends in Biometrics Technology and the Value of Biometric Authentication for
the KYC Process | Steve Cook, Independent Biometrics and Fintech Consultant
Table of contents
3.2 Digital Identity at Border: Between Standardisation and Innovation
Making Sense of Digital Identity | Steve Pannifer, COO, Consult Hyperion
eIDas – Its Role in Our Future | Jon Shamah, Chair, EEMA
Self-Sovereign Identity and Shared Ledger Technologies. A vanguard of a bright new digital identity world,
or an over-hyped innovation? | Ewan Willars, Senior Associate, Innovate Identity
4 The Regulatory Space
A Brief Summary of EBA Guidelines on Fraud Reporting Under the PSD2 | Irena Dajkovic, Partner of DALIR Law Firm
Reconciling Consent in PSD2 and GDPR | Niels Vandezande, Legal Consultant, Timelex
Bitcoin and AML: Regulating the New Mainstream | Nadja van der Veer, Co-Founder, PaymentCounsel
5 Fraud Detection, Identity Verication & Online Authentication –
Mapping and Infographic
5.1 Introduction
5.2 Fraud Detection, Identity Verication & Online Authentication – Infographic
5.3 Fraud Detection, Identity Verication & Online Authentication – Mapping of Key Players
6 Company Proles
7 Glossary
Fraud Management –
Trends and Developments
Overview on the Innovation Taking Place
in the Fraud Management Space
Machine Learning and Articial Intelligence
Mirela Ciobanu | Senior Editor | The Paypers
The lines are blurring between man and machine. As advances in AI, smart tech, and machine learning turn science
ction into fact, a future once fantastical draws near now. How will the payments industry harness these mindblowing
Articial intelligence and machine learning have a wide array of applications, from improving customer experience to ena
bling businesses to ght fraud, from driving the creation of personalised shopping/user experiences by analysing multiple
data points to enabling businesses to stay compliant with the ever changing regulation landscape – KYC, AML. Moreover,
these emerging technologies have also been applied in medicine; popular AI solutions such as IBM’s Watson are actively
used in multiple cancer research hospitals, and they operate as a doctor’s assistant.
However, in this subchapter we will mostly focus on the ways in which these technologies can help ght fraud, manage
and mitigate risk, and enable companies to stay compliant with AML laws and ght transaction laundering.
Articial intelligence
Articial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast
to the natural intelligence displayed by humans and other animals. AI augments human intelligence and should provide
explanations to avoid erroneous interpretations, and its value should be considered in context, as denitive answers do
not exist, according to Pedro Bizarro, Chief Science Ocer, Feedzai.
AI design principles should be transparency, controllability, and automation. Moreover, data provenance is a crucial feature,
as the user needs to keep track of data in order to be able to reconstruct it, and models should learn from real data, and
be able to relearn, while not being inuenced/based on previous models. Most importantly, we must create the means of
developing this tool in order for it to be human-enabled and human-centric.
According to Forbes, AI needs to be ‘Explainable’ and ‘Understandable’. Explainable AI is the domain of data scientists
and AI engineers, the individuals who create and code articial intelligence algorithms. These specialists aim to develop
new algorithms that explain intermediate outcomes or provide reasoning for their solutions.
Understandable AI combines not only the technical expertise of engineers with the design usability knowledge of UI/UX
experts, but also the people-centric design of product developers. Explainable AI is dierent from understandable AI.
Since AIdriven solutions need to be developed with ‘userrst’ principles in mind, understandable AI has become the
domain of UI/UX designers and product developers, in collaboration with AI engineers and data scientists.
Critical to the understandable AI process are the integration of non-data scientists to the development and design of
AI products and enabling people to be a part of the decision-making process in an AI-driven enterprise.
The Rise of Machine Learning/
Articial Intelligence in Fraud Detection
The Rise of Machine Learning/
Articial Intelligence in Fraud Detection
To begin the journey towards a truly humanmachine collaborative model that creates understandable AI outcomes,
leaders, governance bodies, and companies must:
- develop intuitive user interfaces – by using voice recognition and natural language processing, the technology
industry is currently developing AI user interfaces that enable people to interact with intelligent machines simply by
talking to them. By encouraging the development of these tools, the democratisation of AI technologies is encouraged;
- create ethical principles for AI – all major stakeholders in the future of AI need to work together to build principles that
embed understandability into technology development;
- apply design principles – enterprises should use design-led thinking to examine core ethical questions in context. In
addition, they are advised to build a set of value-driven requirements under which the AI will be deployed – including
where explanations for decisions are expected;
- monitor and audit – the AI solutions used at the enterprise level need to be continually improved through value-driven
metrics such as algorithmic accountability, bias, and cybersecurity.
When it comes to nancial services, articial intelligence can be applied to specic areas such as nancial crime preven
tion, regulatory compliance, and payments. Successful AI projects rely on the deep amounts of research and work that
expertise developers put in, and the application to specic business problems, which can be used in multiple dierent
contexts. A critical element of AI systems is the data on which they are trained – it’s that combination of innovative AI
capabilities and deep domain expertise.
A fundamental concept of AI is machine learning – that is why sometimes these two technologies go intertwined.
Machine learning – an approach to fraud detection and protection
Machine learning, a form of articial intelligence, combines data, context, and feature engineering to allow organisations
evaluate the risk of a particular digital interaction or purchase.
Machine learning is being used at many levels in the online fraud detection market. Some solutions are designed to
run alongside existing capabilities, taking in structured and unstructured data to identify anomalies, while others are
designed to provide a score and information codes that can be used by a real-time policy and decision engine.
A machine learning solution needs access to a big store of historical data to train its models and increase the probability
that it will uncover patterns of new suspicious activity. This technology has the potential to ght cardnotpresent fraud,
chargebacks, account takeover, transaction laundering, and more. Also, machine learning is implemented in solutions
such as device assessment, passive behavioural biometrics, bot detection, phone printing, and voice biometrics.
The Rise of Machine Learning/
Articial Intelligence in Fraud Detection
With the waves of new and evolving fraud, Gartner has observed the increasing need of financial institutions and
enterprise-scale merchants for rapid and complex risk decisions, and businesses are turning to machine learning to gain
the ability to make rapid and eective risk decisions. However, with the increased number of machinelearning systems,
clients are demanding explanations, as well as decisions, with the aim of:
- controlling the machine – a model that explains its logic empowers security managers to adapt the model to evolving
fraud patterns with more speed and accuracy;
auditing the machine nancial institutions and large merchants operate in highly regulated environments. These
organisations need to provide trails of explanations for compliance, to demonstrate that the basis for their decisions is
lawful and ethical;
- trusting the machine – a system is only as powerful as the decisions we entrust it to make. How can we trust that the
machine is nding the delicate balance between good risk management and good CX?
To achieve these goals, Gartner suggests that businesses should ensure that each model they develop incorporates
a capability to explain and, moreover, has a loop that provides feedback on the quality of the explanation. The second
method is to develop two systems – one that makes decisions and another that takes the input from the rst system and
generates an explanation.
Here are some types of machine learning that can be deployed:
- Deep Learning – is a class of machine learning algorithms that use a cascade of multiple layers of nonlinear processing
units for feature extraction and transformation. Each successive layer uses the output from the previous layer as
input. These algorithms learn in supervised (eg classication) and/or unsupervised (eg pattern analysis) manners
and understand multiple levels of representations that correspond to dierent levels of abstraction; the levels form a
hierarchy of concepts.
Ensemble Learning – ensemble methods use multiple learning algorithms to obtain better predictive performance than
could be obtained from any of the constituent learning algorithms alone.
Unsupervised Learning – does not require outcomes, so it can learn without waiting for the completion of a three-
month chargeback reporting cycle, for example. This type of learning often relies on clustering, peer group analysis,
breakpoint analysis, or a combination of these. This enables fraud prevention solutions to detect patterns and anomalies
rapidly within extremely large sets of data.
- Supervised Learning – uses outcome-labelled training data sets to learn. Models include neural networks, Bayesian
classiers, regression, decision trees, or an ensemble combination. Massive amounts of data run through dened
models to assess risk outcomes.
The power of supervised and unsupervised machine learning
There are two approaches that are used mostly by fraud prevention vendors – supervised and unsupervised learning, the
former approach being the most common and widespread.
The Rise of Machine Learning/
Articial Intelligence in Fraud Detection
Maxpay explains briey how these systems interact to identify anomalies (outliers). With the supervised approach, in the
beginning, a risk analyst creates a machine learning model based upon historical data. Afterwards, with new transaction
data, the algorithm creates potentially right baskets: fraud and not fraud. After that, the system collects external signals
such as fraud alerts, chargebacks, complaints etc. Based on that information, the algorithm starts looking for new
unrecorded dependencies. Finally, the model starts retraining. Consequently, all the risk analysts are one step behind the
game, thus the cycle continues, and in time new techniques emerge.
Otherwise, unsupervised learning is regarded as an alternative to supervised learning. These algorithms infer patterns
from a dataset without reference to known or labelled outcomes. Unsupervised learning allows risk analysts to approach
problems with no exact idea about what the result will look like. One can derive structure from data where they don’t
necessarily know the eect of the variables. With unsupervised learning, there is no feedback based on the prediction
results. But it can divide data on the basis of anomalous behaviour and, afterwards, risk analysts can apply well-known
supervised approaches to this data.
Therefore, unsupervised machine learning is more applicable to real-world problems and can help solve them when risk
managers are one step behind the fraudsters.
As fraud prevention services use both rule-based and machine learning approaches, including unsupervised techniques,
we should also consider that there is a signicant dierence between fraud detection systems that directly use machine
learning systems and those that are essentially static, rule-based systems. Characteristics of the former type include
exibility in response to new fraud attack patterns. The latter type benets from keeping a human element in the change
control process, which makes it more resistant to skilfully crafted attacks that try to poison the model.
Some banks, merchants, retailers have traditionally relied upon rules-based fraud detection systems in order to counter
threats, such as leveraging weak points through coordinated attacks, but fraud advancements have outpaced the
capabilities of these systems.
According to Feedzai, rules-based systems tend to be either too broad or too narrow in scope to adequately address
fraud attack vectors, requiring nancial institutions to combine multiple solutions into a single system to cover their
Surely, machine learning does not replace rules completely, but it complements them to expand the capabilities of the risk
management platform. Thus, when applied to large datasets, like those found in account opening analyses, these algo-
rithms can pinpoint surprising and unintuitive fraud signals.
The increasing popularity of online shopping is creating new
security risks in the transaction process. Data theft and payment
fraud are issues that consumers and merchants alike fear. If
we look at the current status of online fraud, we see that data
breaches still represent a prevalent issue. Moreover, according
to a research by the Identity Theft Resource Center and
CyberScout, 791 data leaks were reported from large companies
in the US from January to June 2017, with criminals stealing
credit card information amongst other things. This represents an
increase of 29% over the rst half of 2016 and exceeded the
781 cases reported for the full year 2015 in just six months.
Other studies conrm the trend: according to information service
provider Experian, the number of data leaks in ecommerce
increased by 56% in 2017 compared to 2016.
Risk-based instead of rule-based
In the ght against fraud, payment service providers (PSPs) must
have better tools at their disposal than ever before. Rule-based
fraud prevention is replaced by risk-based fraud prevention.
The dierence: previous procedures allowed the risk assessment
to be based on certain rules according to which a transaction
was approved or rejected. The criteria were, for example, in
which country the buyer uses a credit card, whether the device
with which he pays online is unknown to the system, whether he
uses the card several times at short intervals, and whether he
exceeds a certain amount of money when paying. In practice,
many other rules apply but, despite their complexity, they do
not protect against fraud as eectively as the machine learning
method does.
The new generation of risk management that has been used
at Computop since the end of October 2018 is not only more
exible than before, but also more secure and ecient. The new
Fraud Score Engine uses machine learning to automatically
optimise fraud prevention and it eliminates the need for manual
intervention. The algorithm behind the risk cost calculation
learns with each transaction and improves the accuracy of the
risk assessment accordingly. If buyer behaviour changes and
new fraud scenarios emerge, it adapts. A concrete example
illustrates this method:
Previously, the retailer made a yes/no decision in which various
factors were queried, for example: ‘If a transaction exceeds the
amount X and is made in country Y, it is rejected.’ On the other
hand, an intelligent fraud scoring engine calculates probabilities:
‘What proportion of all fraud cases recorded to date deal
with amounts greater than EUR 500, and what percentage
of successful, clean payments is greater than EUR 500?’
This results in a data record that the system uses to calculate the
probability of fraud. This method is much more accurate than
the rule-based approach and can be applied to all parameters
(payment location, device used, etc) that also use rule-based
fraud prevention. The accuracy of the calculation improves with
every payment transaction because, based on the empirical
values from past transactions, the precision of the probability
calculation for each individual parameter increases, thus the
quality of the overall statement increases as well. Essentially,
this is the greatest benet of risk-based fraud prevention.
Machine Learning Against Online Fraud: The Advantage of a Risk-Based Approach
Ralf Gladis | Co-Founder and CEO | Computop
About Ralf Gladis: Ralf Gladis is the Co-Founder and CEO of the international payment service provider
Computop the payment people. In addition, Ralf acts as non-executive Director at Computop, Inc in
New York. He is also responsible for the international expansion and strategic planning at Computop.
Click here for the company profile
About Computop: Computop oers local and innovative
omnichannel solutions for payment processing and
fraud prevention around the world. For ecommerce,
at POS and on mobile devices, retailers and service
providers can choose from over 250 payment methods.
Computop, a global player with locations in Germany,
Canada, the UK and the USA processes transactions
for more than 15,000 retailers annually, with a combined
value of USD 31 billion.
Adaptable, fast and exible
Combined with all the risk factors taken into account – such
as transaction duration, correspondence between invoice and
delivery address, use of an anonymisation service, and many
more –, the engine calculates a score value within fractions of
a second, which represents the basis for the decision, as to
whether the transaction should be submitted to the card-issuing
bank for protection via 3-D Secure.
If the risk factors regarding fraud represent less than a certain
value, the system does not perform an additional query. In the
case of a medium value, the bank either uses its own checking
system to relieve the customer of entering a password or it
requests the password directly. If the 3-D Secure procedure is
used, the bank also takes over the liability risk from the merchant.
If the score is clearly within the red range, the transaction is
rejected directly.
The risk-based method fundamentally changes fraud prevention.
Until now, rule creation was a manual process based on individual
traders. The automation now increases exibility and it is able to
drive double-track. On the one hand, this approach assesses the
riskbased on traderspecic transaction characteristics, and on
the other hand, it uses the entirety of all anonymous transactions
of the PSP for forecasts.
Therefore, each transaction is protected the best possible
way, on the basis of the past, and subsequently contributes to
further optimisation. In principle, PSPs include both successful
transactions and chargebacks from the acquirer’s settlement les
in their risk analysis. Machine learning enables the scoring engine
to move away from the purely manual adaptation to new threats,
that has been adopted, so far, by organisations. This was time-
consuming, inaccurate, and inexible.
With machine learning, the reaction speed to fraudulent actions
increases, as the retailer can rely not only on his own transaction
data but also on risk assessments from Computop’s past payment
transactions thus, on a signicantly higher overall population.
The combination of machine learning and rule-based risk pre-
vention offers the best possible protection, with experienced
experts monitoring the process and providing the artificial
intelligence with the context it needs, to develop further and
work with the right assumptions.
According to artificial intelligence (AI) pioneer Arthur Samuel,
machine learning is a ‘eld of study that gives computers the ability
to learn without being explicitly programmed.’ For fraud mana-
ge ment, this means that machine learning can detect subtle
emerging fraud patterns that are impossible to see on a human
level. Virtually, all fraud management systems today use some
form of machine learning, so what sets CyberSource Decision
Manager apart?
Importance of data: Decision Manager has had machine learning
from the beginning. Decision Manager is the only machine learning
fraud solution that draws insights from Visa and CyberSource’s
68B+ annual transactions processed from around the globe.
These transactions come from tens of thousands of merchants
across a wide variety of industries and specialities. With this depth
and breadth of data, it’s like having more high-quality neurons in
the machine learning ‘brain.’ It just makes sense that better data
leads to better fraud detection decisions.
Why rules are needed: Another very important distinction
with Decision Manager is the inclusion of powerful rules, which
adds a level of precision control for Risk Analysts. But why are
rules important? Let’s explore a theoretical example of what can
happen without rules in the following diagram.
Line L1 shows revenue growth before applying a fraud pre
vention tool. In the diagram, line L1 represents a theoretical
revenue growth trajectory.
Line L2 shows fraudulent activity as a percentage of revenue.
As revenues grow, if fraud losses are left unchecked, they too
would continue to grow as a percentage of revenues, as shown
on line L2.
Line T0 represents the point in time when an organisation
imple ments a fraud management solution. Once a business
realises they have significant fraud losses, they will institute a
fraud mana ge ment system as shown at time T0.
Line L3 shows the reduced level of fraud by using a fraud
mana ge ment programme. As the fraud management system
starts learning from that business’ transaction data, the fraud
loss level should gradually reduce as shown on the red line L3.
Why Implement a Fraud Management Solution that Combines Machine Learning
with Rules?
Mark W. Hall | Sr. Director Global Solutions Marketing, Fraud Management | CyberSource
About Mark W. Hall: Mark is a seasoned entrepreneurial leader who is passionate about crafting multi-
channel marketing programmes that communicate dierentiation and clarity in the Enterprise B2B
space. At CyberSource, Mark heads global cross-functional marketing, positioning, and messaging for
the company’s fraud solutions.
Why implement a fraud management solution
that combines machine learning with rules?
Fraud solution
implementation point
© 2018 Visa, Inc. All rights reserved
Theoretical fraud
loss trajectory
without fraud
Lost sales
Managed fraud is reduced
but never goes to zero
Restored sales
About CyberSource: CyberSource is a global, modular
payment management platform built on secure Visa
infrastructure, with the insights of a USD 427 billion
global processing network. It helps businesses enhance
their customer experience, grow revenue, and mitigate
risk. For more information, visit cybersource.com
It is virtually impossible to prevent all fraud; however, through
active fraud management, the fraud percentage can get very low.
Line L4 represents the reduced level of revenue due to a poor
customer experience while managing fraud. False positives can
lead to lost revenues, as shown on the yellow line L4, not only due
to the loss of the immediate sale, but even more by potentially
losing a customer forever due of the rejected transaction.
This has the impact of reducing revenue growth not only by
interfering with business one transaction at a time, but tarnishing
the expe rience for a legitimate buyer and compromising the
lifetime value of customers.
Line L5 shows what active fraud management can do to
restore revenues closer to the theoretical level. By combining
rules with good manual review practices, many businesses
may actually see an increase in revenue that comes very close
to their theoretical revenue trajectory, as seen in the green line
L5. Decision Manager’s rules can be congured to activate at
a specic time of day or date ranges, which can accommodate
a variety of cyclic, seasonal, and periodic sales promotions –
helping maximize acceptance rates and revenues.
Rules provide customised control: By instituting rules, a risk
analyst can inject human intelligence and set common-sense
para meters for their specic business. For instance, if the item
being sold is a low priced digital good, like a picture or a song,
the risk analyst might have a higher tolerance for the fraud risk
score because there is no cost of goods. This is much dierent
than an online retailer of big-ticket luxury items where the cost
of goods is high – and there’s an open market for fraudsters to
easily turn those goods into cash. Obviously, in the latter case,
the risk analyst will want to send questionable transactions to
manual review prior to shipment.
The best of both worlds: Decision Manager employs machine
learning that operates on insights from 68B+ global Visa and
CyberSource processed transactions, enabling fast detection
of emerging fraud patterns, while at the same time offering
powerful rules that enable the injection of human ingenuity.
Machine learning, combined with rules, provides an excellent
fraud management solution.
Click here for the company profile
Traditional brick and mortar merchants are expanding beyond their
four walls to engage with customers through mobile apps, kiosks,
desktops, and other digital platforms. At the forefront of this digital
transformation is the introduction and branding of trademarked
native mobile apps supporting rich features for creating and
managing accounts, earning loyalty points, providing reviews,
engaging with customer support, other customers and more.
While mobile apps for retail are nothing new, many of the rst
generation apps are being replaced with apps supporting creative
and elaborate digital interaction use cases. These new apps allow
merchants and retailers, regardless of sector, to engage with
customers in a digital environment, in order to build brand loyalty
and engagement and drive towards greater monetisation with
enhanced ease-of-use and personalisation.
This shift towards digital economy is fueling the growth of the
mobile payments industry and it’s becoming a beacon for fraud-
sters to attack traditional brick and mortar merchants. In fact, The
Mobile Payments and Fraud: 2018 Report stated that detecting
fraudulent orders is one of the top three challenges for merchants
in the mobile channel.
Card Present versus Card Not Present =
As brick and mortar merchants make this digital transformation
and begin to accept card-not-present and mobile ecommerce,
they become exposed to all types of fraud schemes and charge-
back programmes that can cause disruption and large nancial
and brand loyalty losses.
When brick and mortar merchants experience fraud in their
traditional card-present environment, the liability of loss is
generally on the card issuer if the merchant supports EMV
transactions. In a card-not-present (CNP) environment, however
(online, mobile web, or mobile app), the liability for a fraudulent
transaction now falls to the merchant. This places the merchant
at risk of new fraud tactics, potential chargebacks, and greater
nancial losses.
With a new focus on creating digital accounts for their customers,
traditional brick and mortar merchants are also exposed to all
types of new fraud, including:
Account takeover: Gaining access to an established digital
account using compromised credentials (username and pass-
word) allows a fraudster to take advantage of the value of that
account. This may include using the saved payment method or
loyalty points to make purchases.
Loyalty reward points fraud: Because reward points can work
like cash, fraudsters identify weaknesses in the system and steal
reward points to sell them.
eGift cards fraud: Considered low-hanging fruit, electronic gift
cards are easily converted into cash, a key requirement for fraud-
sters. They sell them at a discount, with the merchant respon sible
for the resulting chargebacks and any merchandise or services
provided for the value of the gift card.
Promotion fraud: Launching a promotion can often capture the
attention of fraudsters who are skilled at identifying ways to get
around policies or oer limits.
Brick and Mortar Navigates Digital Transformation
Don Bush | Vice President of Marketing | Kount
About Don Bush: Don is the Vice President of Marketing at Kount. Prior to joining Kount, Don was the Director
of Marketing at Cradlepoint, a leading manufacturer of wireless routing solutions in the mobile broadband
industry. Don has worked in several management roles within the technology segment for over 20 years with
both hardware/software manufacturers and as a partner in two top technology-marketing agencies.
Approach to fraud protection
Brick and mortar businesses navigating towards a digital
transformation need to deploy a fraud strategy that is multi-
layered and specically accounts for cardnotpresent fraud.
An underpinning technology for stopping CNP fraud is machine
learning. Machine learning combines data, context, and feature
engineering to allow organisations to evaluate the risk of a
particular digital interaction or purchase. Machine Learning, a
form of articial intelligence, allows fraud prevention solutions to
“learn” on their own and continually improve results. In order to
stop a card-not-present payment, there are two critical types of
machine learning that, when combined, provide the best fraud
prevention foundation.
Unsupervised Machine Learning. Unsupervised learning does
not require outcomes, so it can learn without waiting for the
completion of a three-month chargeback reporting cycle. This
type of learning often relies on clustering, peer group analysis,
breakpoint analysis, or a combination of these. This enables
fraud prevention solutions to detect patterns and anomalies
rapidly within extremely large sets of data.
Supervised Machine Learning. Supervised learning uses
outcome-labelled training data sets to learn. Models include
neural networks, Bayesian classiers, regression, decision trees,
or an ensemble combination. Massive amounts of data run
through dened models to assess risk outcomes.
Brick and mortar merchants that deploy a mobile app need to
account for a new world of risk through digital fraud attacks. There
are great benets to investing in digital engagement channels,
however, with those opportunities comes risk. By addressing
fraud with a holistic strategy, merchants can authenticate a user,
identify fraudulent behaviour, and stop fraud before it inuences
the bottom line and diminishes the merchant’s brand. By building
a level of fraud prevention in their mobile apps, brick and mortar
merchants are empowering decision makers with data to make
informed decisions and to mitigate fraud before it impacts the
businesses’ bottom line.
Click here for the company profile
About Kount: Kount’s award-winning fraud management,
identity verication and online authentication technology
empowers digital businesses, online merchants and
payment service providers around the world. With Kount,
businesses approve more orders, uncover new revenue
streams, and dramatically improve their bottom line all
while minimizing fraud management cost and losses
and protecting consumers. Through Kount’s global
network and proprietary technologies in AI and machine
learning, combined with policy and rules management,
companies frustrate online criminals and bad actors
driving them away from their site, their marketplace and
o their network.
As fraudsters follow the growth of the cashless economy online,
anti-fraud companies are building powerful tools and techniques
that mine various data for fraudulent behaviour patterns.
Fraudulent attacks are getting to be more sophisticated and inven -
tive. Once a new solution against fraud is developed, fraudsters
imme diately nd a new loophole. And it seems that the risk pro
fessio nals are always a step behind.
Machine learning can be used to help solve this problem, but at the
moment it is impossible to completely abandon human interven tion.
Rule-based and machine learning approaches complement each
other because machines can analyse a larger volume of characte-
ristics, based on the context, while risk analysts can create models
that are easily understood by humans, unlike the machine-learning
approach alone. Each industry has its own unique set of features
and each fraud prevention system aims to adapt them to avoid
false positives (good customers identied as fraudsters) and false
negatives (fraudsters identied as good customers). Moreover, the
risk system needs to periodically be examined by risk managers
and afterwards tuned, for example, if online merchants sell new
products or make frequent changes to their billing logic.
The power of supervised and unsupervised machine
A machine learning solution needs access to a big store of histo-
rical data to train its models and increase the probability that it
will uncover patterns of new suspicious activity. The more data,
the better the system becomes at detecting and preventing fraud.
The machine learning process contributes to the learning of non-
linear combinations of latent characteristics and their combi nations
that lead to predictiveness enhancement.
There are two approaches that are used in machine learning:
supervised and unsupervised learning. The rst approach is the
most common and widespread.
With the supervised approach, in the beginning, a risk analyst cre-
ates a machine learning model based on historical data. Then, with
new transaction data, the algorithm creates potentially right
baskets: fraud and not fraud. After that, the system collects exter-
nal signals such as fraud alerts, chargebacks, complaints etc.
Based on that information, the algorithm starts looking for new
unrecorded dependencies. Finally, the model starts retraining.
Consequently, all the risk analysts are one step behind the game,
thus, the cycle continues and with time new techniques emerge.
Unsupervised learning is regarded as an alternative to supervised
learning. These algorithms infer patterns from a dataset without
reference to known or labelled outcomes. Unsupervised learning
allows risk analysts to approach problems with no exact idea
about what the result will look like. One can derive structure from
data where they don’t necessarily know the eect of the variables.
With unsupervised learning, there is no feedback based on the
prediction results. But it can divide data on the basis of anomalous
behaviour and then risk analysts can apply well-known supervised
approaches to this data.
Next Generation Fraud Prevention Platforms Leverage ML to Secure Payments
Pavel Gnatenko | Risk Management Expert | Covery
About Pavel Gnatenko: Pavel has a master’s degree in intellectual systems for decision-making.
He is a risk management expert with more than seven years of experience in the fintech industry.
Currently, Pavel is focused on developing Covery - next generation of risk management platforms.
About Covery: Covery is a global risk management
platform helping online companies solve fraud and
minimise risk. We focus on the universality of our
product and its adaptation to any type of business,
based on the individual characteristics and customer
needs using both rule-based and machine learning
Therefore, unsupervised machine learning is more applicable to
real-world problems and can help to solve them when risk mana-
gers are constantly one step behind the fraudsters.
Why use machine learning in payment fraud
When it comes to detecting and ghting online payment fraud,
several advantages become evident:
- it facilitates real-time decision-making and improves the expe-
rience for customers;
it improves accuracy of classication;
- it helps detect new fraudulent behaviour;
- it provides a more rapid response to real-world changes.
What can the best fraud prevention solutions do
The most advanced fraud prevention services use both rule-
based and machine learning approaches, including unsupervised
techniques, with an industry focus and an adaptation for the
business’ individual characteristics and customer needs. The result
is a solution that makes more accurate decisions for each industry
and every customer. One of the companies working in this space
is called Covery. Risk analysts can customise any combination of
data patterns we call ‘features’ that can be applied to a specic
business needs. Covery can also accept any non-payment data
in any user action to supplement the prole with missing details
to analyse by using both rule-based and machine learning models
for more precise decisions.
So what is Covery?
Covery is a global risk management platform helping online com-
panies solve fraud and minimise risk. The company focuses on
the versatility of the product and its adaptability to each type of
business, based on the individual characteristics and customer
needs using both rule-based and machine learning approaches.
Covery works with high-risk as well as with low-risk industries to
nd the right solution for every customer.
What Covery oers to help with fraud prevention:
- wider coverage of user actions for analysis;
 exible customisation of data patterns;
- usage of any additional data for analysis;
- rule-based and machine learning approaches;
- functionality to work with loyal users to increase revenue;
- custom machine learning models creation;
- custom functionality upon request;
Fraudsters are always developing new tricks and risk managers
don’t always have the time to adapt to new changes. Machine
learning has long been expected to help solve the problem of
preventing fraud, but the majority of solutions are still on the path
of development. So Covery’s main goal is to solve the problem
when risk managers are constantly one step behind the fraudster.
Click here for the company profile
3 - 5 June 2019, The Rai
Europess biggest FinTech
event is back in Amsterdam.
M20E19_PRINT_v2 ADS.indd 1 11/29/18 4:30 PM
Best Practices in the Fraud Management Space
Goodbye fraud, hello customer experience
If the headline to my editorial caught you by surprise, let me
explain. While we’re not kissing ecommerce fraud completely
goodbye anytime soon (courtesy of those increasingly organised
fraudsters, confused customers, and savvy consumers looking for
ways to game the system), the payments industry is continuing to
direct its focus toward the far more lucrative domain of ‘customer
If 2018 has shown the payments community one thing, it’s that
we’re at a critical inection point and moment of decision as an
ecosystem. As I’ve talked to payments professionals this year and
closely followed the lightning-fast pace of change, the nature of
this key ‘moment’ is coming into sharp focus.
The pendulum shift from fraud to customer expe-
The CNP fraud conversation continues to shift increasingly to
defining moments of customer experience. While fraud is no
longer the central concern, it’s still very much part of the picture
as the industry continues to cope with a rampant ‘friendly fraud’
(or false claims) problem. Ethoca’s assessment is that the CNP
chargeback problem is estimated at USD 50 billion, comprised
of a combination of blended OPEX for both merchant and card
issuer, and lost value on transactions that are falsely disputed
by cardholders (sometimes unwittingly, but increasingly abusive
in nature). As a blended average across all merchant categories,
friendly fraud is hovering in the 30 to 40% range, but it’s most
acutely felt in digital goods where it can exceed 90%.
The most staggering fact is that while USD 50 billion is a headline-
grabbing number, it pales next to the lost transaction value and
customer insult factor that comes with false declines – when good
transactions are falsely rejected due to apparent fraud risk. Aite
Group estimates that false declines are costing the industry USD
331 billion annually, and that number is set to rise as the pervasive
inuence of friendly fraud continues to wreak havoc with eective
fraud decisioning.
The compounding regulatory ripple eect
One of the biggest ironies of 2018 is that the rise in customer
experience together with cardholder protection are reaching a
crescendo just as the regulatory environment is about to kick into
motion a series of changes that will potentially make it harder than
ever to create a frictionless customer experience. Enter PSD2 –
particularly the Strong Customer Authentication (SCA) component
of the updated payment directive release by the EBA.
When two-factor authentication becomes mandatory on all trans-
actions over EUR 30, the industry will be waiting with bated breath
to measure the impact of customer conversion and declines.
It’s important to remember that potentially 30% of all customer
declines are never tried again on another card in the cardholder’s
wallet. And while SCA exception scenarios exist when fraud rates
can be held in check at a PSP or acquirer level, it will prove to be
very challenging for ecommerce merchants to realise that benet
with so many false claims in the system.
Keith Briscoe | Chief Marketing Ocer | Ethoca
About Keith Briscoe: Keith Briscoe leads Ethoca’s global product and marketing functions, a role
spanning the development of Ethoca’s suite of collaboration-based fraud/chargeback mitigation and
transaction acceptance solutions, as well as integrated marketing programmes. His mandate includes
product strategy and management, new product innovation, competitive analysis, experiential marketing,
integrated marketing campaigns, public relations, analyst relations, content strategy, and stakeholder
Collaboration Paving the Way for Ecommerce Customer Experience
Click here for the company profile
About Ethoca: Ethoca is the leading provider of
collaboration-based technology that closes the
information gap between thousands of card issuers
and ecommerce merchants worldwide including
the top global brands and banks. Ethoca’s powerful
suite of innovative solutions help stop fraud, eliminate
chargebacks, improve customer experience and
increase card acceptance.
3DS 2.0 holds the promise of delivering higher acceptance rates
as long as merchants can get comfortable with sharing extended
data elds with card issuers to benet from liability shift. However,
in parallel with this key question, there is a lot of chatter about the
‘death of fraud detection’ given that merchants can simply accept
every transaction and let 3DS liability sort out the rest. That would
be a tremendously short-sighted move, ultimately straining the
delicate card issuer – merchant acceptance balance.
For a start, this approach would trigger more step-up authenti-
cation at the card issuer, introducing increased friction – and
abandonment – into the purchase process. In addition, facing
increased losses as a result of liability shift, card issuers’ accept-
ance and fraud detection models would likely decline more.
Once again, we’re seeing all of this potentially set the stage for
anything but a good customer experience. Creating customer
habituation will be key (ease of use, minimal friction and virtual
invisibility). But it must be balanced with responsible and equitable
behaviours from both merchants and card issuers and enabled by
innovative technology that encourages productive, value-based
The case for collaboration
So where is all this heading? During no other period in the history
of payments has the time been more right for industry collabo-
ration to solve the most pressing problems in ecommerce. The rise
of what we at Ethoca call ‘bi-lateral rich data exchange’ is opti-
mally positioned to solve these increasing challenges. Here are
three recommendations for solving the most pressing customer
experience and transaction acceptance challenges heading into
1. Take the noise out of the system – The tricky thing with
friendly fraud is that it’s virtually impossible to detect with typical
fraud detection tools because it’s largely behavioural in nature.
It simply doesn’t ‘look’ like fraud, because it isn’t. Making
merchants’ deep purchase and account insight available to
card issuers’ mobile applications and to call centre agents –
at the pivotal moment of customer concern is a critical rst
step in helping customers understand what they bought. The
result: better fraud decisioning (less garbage in means higher-
performing detection systems), fewer false declines, fewer
fraud claims, and improved customer experience.
2. Set the stage for ‘post transaction customer experience’
Utilising rich data and intelligence sharing between card issuers
and merchants to solve for dispute challenges is just step one.
Think about where this goes from here: when cardholders
have access to their consolidated digital receipts in the bank’s
mobile app, that’s where customer experience enters ‘next
level’ territory. That digital journey should matter as much to
banks as it does to merchants, laying the foundation for highly
relevant cross-sell opportunities and deeper engagement over
the course of the purchase journey.
3. Build the business case incrementally – One of the biggest
challenges in realising the full potential of bi-lateral rich data
exchange between card issuers and merchants is nding the
‘wedge’ use case(s) that prove the value through an incremental
approach. Ethoca’s view is that by starting with the biggest
pain points – moments of dispute or concern that can be
in stant ly resolved with real-time intelligence ‘in the moment’
– card issuers and merchants alike will become increasingly
comfortable with sharing intelligence that drives the best
possible customer experience.
At Ethoca, we’re welcoming 2019 with open arms and excitement:
the times, it seems, have caught up with collaboration.
Let’s start with payments. What do European mer-
chants need to be aware of when expanding over-
In the US, payments reflect consumer behaviour. There are
generally fewer standard payment methods than in Europe, and
the majority of payments are made via credit card rather than
direct debit or money transfer.
These payment types may present some diculties from a fraud
perspective for example, making it more dicult to claw back
disputed funds.
One economic factor is the interchange fee on credit cards. Unlike
Europe, the US does not have a cap on these charges, which is
why the average interchange fee in the US is 1.73%, compared
to 0.96% in Europe. Interchange fees on debit card transactions
were capped in 2011 by the Durbin Amendment, but this does not
apply to credit cards.
How does Europe compare to the US from a fraud
prevention perspective? How do the strategies for
combatting fraud dier?
In some respects, Europe and the US are similar when it comes
to payment fraud. The majority of merchants on both sides of the
Atlantic review fewer than 10% of transactions and the reject rate
is around 3%.
The overall fraud rate in the US is higher though. One reason
for this is that in Europe fraud patterns are more recognisable,
since they tend to come from specic countries and merchants.
In the US, fraudsters have more opportunity to blend in and nd
sophisti cated ways to get around prevention mechanisms.
It is also easier for fraudsters to build proles for fraud due to the
availability of data in the US, where the focus tends to be on pay-
ment validation rather than identity verication.
Felix Eckhardt | CTO | RISK IDENT Piet Mahler | COO | RISK IDENT
About Felix Eckhardt: Felix Eckhardt was with RISK IDENT at its inception. Initially taking
up the position of senior software engineer, he helped RISK IDENT get on its feet as the
chief architect behind the company’s second fraud prevention product, FRIDA. A year
after the company’s founding, Felix became the CTO and remained in the position until
he moved to Australia in 2016. While abroad, he acted as Senior Software Developer,
developing data-driven solutions for telecoms and marketing industries for two years.
About Piet Mahler: Piet Mahler is the COO at RISK IDENT, leading the strategic direction
of the company alongside the CTO, Felix Eckhardt. He is responsible for the development
of the business side of the company, having previously held the position of VP Operations
& Business Development, helping lead the company’s international growth.
Felix Eckhardt, Managing Director (CTO), and Piet Mahler, COO, RISK IDENT consider some of the key payment, fraud prevention,
operational, and regulatory issues for European merchants with aspirations of doing business in the US.
The majority of merchants on
both sides of the Atlantic review
fewer than 10% of transactions
and the reject rate is around 3%.
Click here for the company profile
Data protection is also a consideration in the US, where individual
states often have their own rules, in addition to national standards.
For example, the FCC is in charge of the rules concerning what
data internet service providers can and can’t sell; health data
is protected under the federal Health Insurance Portability and
Accounta bility Act, and the Federal Trade Commission enforces
the Children’s Online Privacy Protection Act.
Ecommerce in the US is worth almost half a trillion dollars annually,
according to the US Commerce Department. In Europe, it is worth
over half a trillion euros and growing fast.
Cross-border commerce is the Holy Grail for retailers; tune your
fraud prevention today to ensure it doesn’t become the same for
the fraudsters.
European merchants tend to rely on vendors for fraud decisions,
whereas in the US merchants rely on the vendors for the platform
and the merchants figure it out themselves. This seems to be
especially true for larger merchants.
Our research has found that the variety of fraud reporting struc-
tures in the US is quite pronounced. These reports address die
rent corporate priorities and have a general lack of consensus.
This is usually how vulnerabilities that can then be exploited open up.
What operational considerations should merchants
focus on when expanding overseas?
US consumers are demanding. Many will make purchases
during their commute and they expect next day delivery from
all merchants, even those based outside the US. Many of them
will not consider where the merchant is based when making a
purchase online. Having a US fullment house is a consideration.
In Europe, it is critical to oer local payment options to keep con
ver sion rates high. Consumers expect to be able to pay with all
major payment types with national dierences. Missing payment
types lead to abandonment.
GDPR came into eect this year. How does the US
dier from Europe when it comes to regulation?
There has been a great deal of talk about the General Data Protec-
tion Regulation (GDPR), but European data privacy rules and
attitudes have long been far stricter and more discerning than in
North America.
The other big change in online commerce in Europe is the Second
Payment Services Directive (PSD2). Combined with the GDPR, it
provides greater choice for consumers in how they can pay and
control their nances, while also aiming to modernise approaches
to security and privacy.
One dierence is a call for a minimum of twofactor authentication,
whereby a consumer would not just be asked for a password, but
may be asked for either a biometric scan or for authentication via
another device, such as a smartphone. Another example is that US
merchants have collected data just because they could, but this
is an unnecessary risk and in many cases businesses don’t know
what to do with this data. Now they have to inform customers
clearly about the need and how they manage data protection.
About RISK IDENT: RISK IDENT is an anti-fraud
software development company based in the US and
Europe that protects companies within the ecommerce,
telecommunication, and nancial sectors. RISK IDENT’s
machine-learning software uses sophisticated data
analytics to block any kind of fraud, all with human-
friendly user interface that simplify a fraud prevention
team’s decision-making process.
Traditionally, when we talk about the approval of online trans-
actions, merchants are the ones who have the majority of ‘rich’
By that, I’m referring to merchants having access to elements such
as customer demographic info, name, email address, and IP address
of the customer submitting the transaction. Also included is the
shipping address, along with what type of products are being
The hitch in this process is that when merchants request authori-
sation from the issuing bank, those issuing banks don’t have access
to the same data. The data they can see has historically been
very limited. The basic things that issuing banks can see are:
● What is the line of credit for that card?
● Is that transaction over the limit?
● Has that card been used before in that industry?
● Has that card been used at that merchant before?
The transaction amount, and in certain cases the name and billing
address associated with the payment method, which can help in
the authorisation process, may also be present.
Here’s the problem
The lack of visibility for issuing banks into this important customer
information can generate signicant impacts on the authorisation
process. These eects are especially magnied in the Central and
South American markets, where a very large percentage of online
transactions are declined, even reaching 20% or more in certain
In the US, the numbers are much lower, but the impact is still there,
nonetheless. There is an exception, though, when the Issuing
Bank is also the Acquirer, meaning they have a relationship with
the card holder as well as the merchant.
These types of relationships allow more data to ow than a simple
credit card and name/address information, such as the email and
IP addresses, and other details about the order, which have proven
to be indispensable in allowing more precise decisions that benet
all parties involved.
For customers, orders are approved more quickly with less disrup-
tion. For merchants, this translates into more revenue, as a larger
portion of orders is approved.
Are You Ready for the New Era of Online Payments?
Amador Testa | Chief Product Ocer | Emailage
About Amador Testa: Amador is Chief Product Ocer at Emailage. He is an industry expert in online
fraud, identity theft and cybercrime. Before Emailage, he was the head of fraud for card acquisitions at
American Express and later led global fraud prevention divisions at Citigroup. Amador enjoys playing
tennis, running marathons and traveling with his family.
Click here for the company profile
About Emailage: Emailage, founded in 2012 and
with offices across the globe, is a leader in helping
companies signicantly reduce online fraud. Through
key partnerships, proprietary data, and machine-learn-
ing technology, Emailage builds a multi-dimen sional
profile associated with a customer’s email address
and renders a predictive risk score. Customers realize
signicant savings from identifying and stopping frau-
dulent transactions.
To learn more, visit: www.emailage.com, @Emailage on
Twitter, or the company’s LinkedIn page.
Big changes to come
There are key changes on the horizon for issuing banks, allowing
them to validate digital identity of their customers.
Version 2.x of the 3D Secure protocol is the rst to require mer
chants to send the email address of customers to the issuer. While
there are many other data elds also included, the email address
is important because it is almost invariably used to confirm
the purchase. This means that if fraudsters use the address
associated with the card, the cardholder will be informed that an
order has been placed. Criminals can avoid this by using accounts
under their control to place orders. Email can, therefore, be a vital
indicator of fraud. But it’s not as simple as checking that the email
address matches that held by the issuer. Globally, it is estimated
that there are 1.75 accounts per email user and this figure is
higher in the developed world with users typically having three
active accounts including a work email address. Spotting a new or
unrelated email address can really help.
It’s also important to know whether a specic address has been
involved in a previous fraud. While email address checking is no
silver bullet for ecommerce fraud, it can be a powerful tool when
combined with other data and analytics during the authentication
or authorisation process.
Risk scoring of email addresses
While using email as a factor in risk assessing payments is new to
issuers, Emailage has a history of helping merchants counter the
threat of fraud in ecommerce. Since 2012, Emailage has oered
fraud risk assessment built around the email address.
We utilise a predictive risk score based on machine learning
algorithms combined with a cross-industry and cross-sector
consortium database. This approach oers merchants the ability
to mitigate fraud with negative signals while using positive signals
to approve good customers. The roll-out of 3-D Secure 2 and the
implications of Strong Customer Authentication in the European
Union will mean that both the obligation and the capability to ght
fraud move to card issuers.
Card issuers are faced with a challenge – how will they balance
customer friction and fraud prevention? The businesses which
have better fraud risk analytics and better data on which to make
decisions will do better. Merchants have already discovered that
email address is an eective fraud risk factor in ecommerce; it is
now time for the nancial services industry to learn lessons from
An increasingly mobile & digital landscape
As mobile transactions now account for 58% of total transactions,
mobile is now fuelling each stage of the customer journey and has
become the preferred method of interaction. Across industries,
almost two-thirds of all account creations now come from a mobile,
whilst in nancial services, mobile transactions make up 61% of all
account creations and 66% of all account logins.
With the global push for digitisation, online transaction volumes
are relentlessly increasing, mimicked by a corresponding surge
in cybercrime and automated attacks. Compounded with the
regu latory push for disclosure, individuals have resigned them-
selves to the dramatic headlines and alarming statistics.
Technology as an enabler: opportunity knocks...
The more consumer behaviours change and adoption of new
techno logies increases - such as machine learning (e.g. AI driven
nancial apps, chatbots), the IoT (e.g. payment wearables, home
assistants) - the more criminals find additional opportunities to
exploit vulnerabilities. Indeed, the 21st century has given fraud sters
an ideal playground with the combination of digital interactions,
the systemic failure of organisations to keep pace with the security
measures needed for new technologies, readily available personal
data that can be harvested from the many data breaches that
have or have not made the news, and the willing ness of many
merchants to relax their risk controls during peak trans action times
to approve more orders (such as during world sporting events or
holiday periods). Moreover, as criminals also have the opportunity
to capitalise on new technologies and automated tools, this melting
pot of opportunity has enabled them to find new ways to hide
behind large transaction volumes, leading to spikes in bot activity
(ThreatMetrix Q2 2018 Cybercrime Report).
A complex regulatory landscape
As payment industry reforms (e.g. 3DS 2.0 and Open Banking
worldwide, or PSD2 in Europe) try to promote innovation and
reduce friction whilst providing secure payment interactions, data
protection regulations (such as the GDPR in Europe or the CCPA
in California) apply even more pressure on businesses that handle
personal data.
To meet the regulatory challenge and manage risk effectively,
organisations must get as close as possible to a single end-to-end
view of the customer, regardless of service/product, channel or
device. And they must do this as seamlessly as possible. In other
words, businesses must be able to distinguish between genuine
customers (who are increasingly ubiquitous) and fraudsters (who
are increasingly able to mimic genuine customers).
The automation era
Indeed, stolen data (and identities) will be used by criminals
for two main purposes: opening new accounts (which can lay
dormant for periods of time and then used to make payments
using stolen card details) and taking over existing accounts (to
purchase goods and services, steal credentials and payment
details). Large ecommerce retailers are a target of choice for auto-
mated bot traffic, which makes use of readily available stolen
identities and capitalise on the fact that individuals will often reuse
passwords across many sites (aka “Credentials Stung”).
Emerging Payments Association
Account Takeover via Hacking Bots (The Rise of the Bots)
Neira Jones | Ambassador | Emerging Payments Association
About Neira Jones: Neira advises organisations on payments, ntech, regtech, information security, regu-
lations and digital innovation. She holds a number of Non-Executive Directorships and Advisory Board
positions and is on the Thomson Reuters UK’s top 30 social inuencers in risk, compliance and regtech
2017 and the Planet Compliance Top 50 RegTech Inuencers 2017.
About Emerging Payments Association: The Emerging
Payments Association (EPA) has over 130 members
from across the payments value chain. We connect the
payments ecosystem, encourage innovation and drive
business growth, strengthening the payments industry
to benefit all stakeholders. Get in touch at info@
emergingpayments.org or +44 20 7378 9890.
Automated bots enable criminals to launch attacks that keep
trying credentials until they match an existing account, with very
little eort.
Source: ThreatMetrix Q2 2018 Cybercrime Report
By contrast, the nancial services industry has always been heavily
regulated, and security and fraud prevention mechanisms are
gene rally stronger than in other industries. It is no surprise there-
fore that the preferred attack method is through social engineering
(e.g. tricking customers into transferring funds to a mule account,
or giving away credentials). A notable exception to this is that
fraudsters see ntech providers as easier targets than traditional
financial services companies due to the fact that fraudsters
attempt to exploit new and emerging platforms to exploit gaps
in process and infrastructure (e.g. “Loan Stacking” - where new
loans are applied for using an inltrated account, using one loan
to pay o the next until the loan value is inated to the maximum
amount available, which is when the criminal defaults on payment),
targeting account logins and payments transactions.
Challenges and opportunities
As consumers continue to adopt new and emerging techno-
logies, the challenge is to balance customer experience with
security. This will mean that businesses will have to ensure that
they deploy dynamic approaches to counter the proliferation
of stolen identity credentials and advanced device and identity
spoong techniques which allow fraudsters to bypass the most
complex online application procedures. Indeed, recognising
legitimate customers across industries and channels will also
fuel growth and opportunities. This also means that businesses
must use a variety of fraud detection and prevention methods,
stop relying on passwords as their top form of authentication and
look beyond retrospective transaction analysis towards real-time
and predictive consumer behaviour analysis, as well as moving
beyond rules to context and attributes. Moreover, the lack of
digital identity integration with wider customer engagement
stra te gies will lead to fragmented customer experiences and
customer attrition, the inability to capitalise on customer data
to inform decision-making and enhance the overall customer
experience, as well as to data privacy challenges. Real-time
solutions combining multiple data points (e.g. device information,
biometrics, contextual, predictive, and behavioural information
etc.) will help businesses better recognise their customers - rather
than challenge them - and will also help identify anomalies such
as account takeover and automated bot trac.
Could you please provide our readers with some
insights into your professional background, prior to
joining MRC?
My degree is in mechanical engineering, so I love to solve
problems. However, I started my payments career as a software
engineer at Ticketmaster, which has grown into the largest and
most comprehensive ticketing platform in the world. We built our
payments and ecommerce platforms from the ground up, for ultra-
high performance and scalability. About midway through my jour-
ney at Ticketmaster, I caught the fraudghting bug, and dedicated
much of my time to making our payments and risk teams work
closely together to disrupt the fraudsters. We developed internal
systems and partnered with other great companies to ght back.
As an ecommerce leader, with extremely high stakes in ghting
fraud, my organisation joined the MRC as a Merchant Member
where I soon became very involved in the MRC community,
engaging as a conference speaker, a committee member, and
ultimately serving on its Board of Directors.
Merchant Risk Council is now a well-known asso-
ciation among fraud and payments professionals,
firmly rooted in the industry. How did everything
start and what problems were the founding members
looking to solve back then?
This whole thing started almost two decades ago. In fact, the MRC
celebrates its 20th anniversary in 2020, and to this day conti nues
its vision of making commerce safe and protable everywhere. It all
began when a handful of online retailers got together to discuss
their challenges in ghting fraud.
The Internet was brand new, with huge potential for sales, and
in turn, created a new channel for criminals to inltrate and take
advantage. This merchant group met in person a few times a year,
and later formed the organisation known now as the Merchant
Risk Council. As ecommerce exploded, so did fraud, and the
demand for online solutions and technology to ght it. The MRC
naturally grew in membership and expanded its reach to include
solution providers, issuers, card brands, law enforcement, and
other industry partners. Today the MRC consists of a diverse mix
of nearly 550 member companies representing a wide variety
of industries, technologies, and services. What’s really cool is
that nearly all the founders are still very involved with the MRC,
either as merchants or solution provider member organisations.
Collaboration started everything and continues to be what it’s all
Paul Kuykendall | CEO | Merchant Risk Council
About Paul Kuykendall: With over 20 years of experience in global payments and fraud technology, Paul
came to the MRC as the VP of Payment Platforms for the world’s largest ticketing company. He is a
subject matter expert on payment processing, data security, compliance, and risk mitigation. Paul’s prior
MRC involvement includes various committees, regional boards, and the Global Board of Directors.
Merchant Risk Council
Paul Kuykendall depicts his vision of MRC’s future growth opportunities and the way this community evolved in order to support
merchants in ghting payments and commerce fraud.
Our mission is engagement
within our community. MRC leads
the industry with information
about ghting fraud, reducing
risk, and optimising payments.
How do you see this industry evolving in terms of
both challenges and innovations and how does this
evolution align with MRC’s plans for 2019?
‘We are the MRC community and together we evolve’ was
the theme of our autumn conferences this year, and we totally
embrace it. The business of ghting fraud is changing at a rapid
pace, and merchants must adapt together. The ntech industry
is bursting at the seams with new and better ways to identify
and stop fraud. The very cool thing about the collaboration that
the MRC generates is that we, as a community, solve problems,
and share the solutions. It’s an arms race, for sure. We know that
fraudsters collaborate. They share tools and resources on the dark
web. They exchange information about what works for them, and
what doesn’t. The best way to beat them is for merchants, large
and small, to work as a team. That’s what the MRC is all about.
What were the key themes on the agenda of US
fraud and payment managers for this year?
Improving the customer experience is an interesting theme that
is emerging from the merchant community and is reflected in
upcoming conference agendas and the ongoing conversation. The
conict between checkout friction and sales conversion is always
a point of discussion. Identifying fraudulent behaviour without
rejecting or offending good customers is critical because the
market is so competitive. Identity verication, machine learning,
deep analytics, and chargeback management are all gaining
prominence in the conversation. But, as always, the focus is on
people getting better at what they do, learning from their peers,
and evolving together with the industry.
How does MRC help new entrants in the industry
cope with the rapid changes in the payments fraud
and risk environment?
Our primary mission is engagement within our community. MRC
leads the industry with information about ghting fraud, reducing
risk, and optimising payments. We oer and are expanding our
online education courses called RAPID Edu, which is short for
Risk and Payments Industry Development Education. This is
a great leg-up for professionals new to the payments and fraud
industry because they can take educational courses at their own
pace, and on their own schedule, at a time convenient to them
day or night. Currently, the MRC oers a Chargeback Essentials
course and will soon be releasing a Fraud Essentials course
followed by a Payments Essentials course in the coming year.
We also encourage collaboration through our mentor programme,
where new folks can meet experienced professionals and get a
quick introduction to key people and concepts that will improve
their skills. Our website is packed with case studies, webinars,
surveys and whitepapers (as well as other relevant content
to help educate) and our community forums spur important
conversations. Last but certainly not least, we oer four annual,
best-in-class conferences in the US and Europe as well as
regional networking events throughout the year. We truly have so
many avenues through which our merchants can learn and grow.
About Merchant Risk Council: The Merchant Risk
Council (MRC) is a global trade association providing a
platform for ecommerce fraud and payments professio-
nals to come together and share information. As a
not-for-profit entity, the MRC provides year-round
support and education to members by oering access
to proprietary benchmarking reports, whitepapers,
presentations, and webinars. The MRC hosts four
annual conferences in the US and Europe, as well as
regional networking meetings for professionals to build
better business connections, exchange best practices,
and share emerging trends. The MRC is headquartered
in Seattle, WA and has an oce in Dublin, Ireland.
Best Practices of Mitigating Fraud
in Ecommerce – the State of Aairs
in Ecommerce Verticals
Mirela Ciobanu | Senior Editor | The Paypers
Ecommerce as a whole continues to be a prime target for monetising stolen identity credentials harvested from data
breaches. Stolen data (and identities) will be used by criminals for two main purposes: opening new accounts (which
can lay dormant for periods of time and then used to make payments using stolen card details) and taking over existing
accounts (to purchase goods and services, steal credentials and payment details).
Once fraudsters have stolen account credentials, they don’t wait around, but use them to commit account takeover
(ATO), Sift Science security specialists warn us. For businesses that experience the highest rates of ATO, a compromised
user’s account activity increases an average of 22x within a week of the takeover. Fraudsters use stolen credentials as much
and as quickly as they can before the user or business redeems control of the account.
As mobile is becoming the key enabler at almost every stage in the customer journey, fraudsters have now realised that
if they perform a SIM swap, or even port out a telecoms account service, they can gain the ability to not only add
services to the telephone account, but also use the phone number to intercept and approve nancial transactions,
compromising both the victim’s nancial services and their telephone account, says Jason LaneSellers, CFCA President &
SIM swap fraud is largely made possible due to the fact that customers are able to switch SIMs while carrying their current
phone number with them. Fraudsters exploit this possibility, calling network operators and posing as the victim claiming
to have lost their SIM card or needing switch to a new provider. If the fraudster successfully passes the security questions
asked by the operator, they will be able to transfer the victim’s phone number over to a SIM card in their control.
Another type of fraud encountered in the online luxury industry is Mail Order/Telephone Order fraud (MOTO). MOTO
is a form of ‘card-not-present’ transaction, where services are paid for and then delivered via the internet, telephone, or
mail. For a Switzerland-based luxury goods holding company, Richemont, this type of purchasing represents 50% of the
transactions, and therefore the risk associated with it is increased, as the MOTO channel is also preferred by fraudsters.
Challenges and recommendations
Some key challenges for ecommerce merchants are: balancing an optimised customer experience with low friction
authentication, shortening processing times for orders, the ability to eectively identify good returning customers,
while also maintaining eective fraud control. Also, with the advent of PSD2 in Europe, businesses need to integrate
riskbased authentication with lowfriction SCA in order to avoid introducing unnecessary friction into the
payment ow.
One way to do this is through device binding, a process that allows users to transact on trusted devices without
repetitive authentications. This occurs through reliable and consistent verication of the transacting device, by
registering the device and binding it with a user credential.
Fraud in Ecommerce – Diagnosis and Treatment
Another way to understand potentially high-risk scenarios in ecommerce/chargeback situations is to create a unique digital
identier for every transacting user, and visualise the relationships between all the entities linked to that user, such as device
information, tokenized email address, and other account markers.
Enterprises need to ensure they have dynamic, behavioural analytics-based fraud detection systems in place, which can
both identify good returning customers in unusual situations (such as travelling abroad to the World Cup/ Winter Olympics),
as well as spotting fraudulent use of credentials, which criminals try to mask by hiding in unusually high transaction volumes.
Fraud and risk managers should also take into account quantifying the revenue impact of false positives and poor customer
experience due to legacy techniques and policies aimed at reducing fraudulent events. They are advised by Gartner to
consider an expanded ROI calculation to increase revenue opportunities, as well as reduce potential fraud losses.
Fraud in Ecommerce – Diagnosis and Treatment
Sift Science is a technology vendor for online travel
agencies (OTA) that seek to fight fraud. Can you
portray your typical customer?
Our typical customers are companies seeking an innovative
tech nological approach to fighting fraud, while also placing
equal importance on maintaining an excellent user experience.
Customers who have something of the best results with us tend
to operate with low-margin, high-volume, instant-delivery busi-
ness models. They also often have lean fraud teams and rely
heavily on automation.
What are these customers currently doing wrong in
stopping fraud and what are the challenges they are
In the online travel space, fraud teams must make accurate real-
time decisions for high average order amounts, looking at users
that are new to the system or don’t make bookings very fre-
quently. This is very challenging, because you don’t have as
much data on these travelers, and there is high financial risk
involved in every decision.
Many fraud prevention vendors only look at transaction data, which
results in lower accuracy. Behavioral data is extremely valuable
for preventing fraud. Imagine this scenario: a legitimate travelers
buys ights to Barcelona, spending time browsing for the best
deal, choosing seats, checking out hotel packages, and sending
the itinerary to family members. It takes a while. In contrast, a
fraudster may complete the entire shopping process in two
minutes and then log out.
Legitimate users rarely bother to log out of websites. The timing
and logging out are two signals that could point to fraud.
Other vendors also use rules that don’t scale, are static, and don’t
adapt to changing fraud patterns. At Sift, our real-time machine
learning based on an ensemble of models and 16,000+ signals is
a real dierentiator.
How do loyalty programs work in this industry and
how do fraudsters exploit them?
Forget bitcoin – loyalty points are the original digital currency.
Loyalty programs create nancial liability for companies, since
so many travelers accumulate large unused balances. These
balances are attractive targets for fraudsters, since they’re easy
to drain, and you don’t need to input payment info to redeem the
points. Loyalty fraud is a growing crime, with 11% of cardnot
present fraud attacks on loyalty and rewards points accounts in
2017 – up from 4% in 2016.
Kevin Lee | Trust and Safety Architect | Sift Science
About Kevin Lee: Kevin Lee is driven by building high performing teams and systems to combat
malicious behavior. He has worked for the last 10+ years around developing strategies, tools and teams
responsible for billions of users and dollars of revenue. Prior to Sift Science, Kevin worked as a manager
at Facebook, Square and Google where he lead various risk, chargeback, spam and trust and safety
Sift Science
The Paypers sat down with Kevin Lee, Trust & Safety Architect at Sift Science, to nd out the latest trends and developments
in ghting loyalty fraud in travelling industry.
Loyalty programmes
create nancial liability for
companies, since so many
travellers accumulate large
unused balances.
Click here for the company profile
In a typical scheme, a fraudster will use stolen login credentials
obtained from a data breach or hack to gain access to a
traveler’s account. Then, they use the “transfer points” option to
liquidate the balance. A fraudster may also use stolen credit card
information to purchase multiple airline tickets, accumulating
a huge amount of loyalty points and quickly redeeming them
before the crime is discovered.
Unfortunately, most loyalty programs have minimal security in
place to curtail this abusive activity in order to provide the most
friction free customer experience as possible. In fact, many
companies choose to whitelist these customers in order to
circumvent any security checks, which is especially problematic.
How are companies in the travel industry currently
ghting/preventing these problems? Does a solution
for preventing loyalty and travel fraud truly exist?
Some solutions that travel companies use to prevent loyalty
fraud include:
Setting limits and rules on how fast customers can earn points
and spending requirements to accrue points
Establishing manual review teams to spot abusive behavior
Checking customer point transactions histories, looking for
how long and at what pace a person accrued points, as well as
how fast those points were spent
Introducing 3D Secure or other verication methods
However, these solutions not only negatively impact the custo-
mer’s experience – customers don’t want to be made to spend a
minimum in order to accrue points or have to remember a pass-
word to verify their identity – they also require more labor and cost
on the merchant’s end. Sixty percent of online businesses are
concerned about spending too much on manually review ing
A true solution to preventing fraud is multi-layered. It’s not just
about eliminating fraud, but more about limiting exposure and
enabling your top line to grow. At the foundation is the ability
to ingest a high volume of data from all stages of the customer
journey. Then, you need sophisticated technology like real-time
machine learning to uncover patterns in the data, so you can
both automate accurate decisions and empower your review
team to take the right action on gray-area cases.
About Sift Science: Sift Science is a machine learning
company that fuels business growth by empowering
world-leading online businesses to drive risk-free
user experiences. Sift dynamically prevents fraud
and abuse by combining industry leading technology
and expertise, a global data network and long-term
customer partnership. Global brands such as Twitter,
Airbnb, Yelp!, Shutterstock, Jet.com, Indeed and Wayfair
rely on the Sift Science Digital Trust Platform for access
to a global network of fraud data, 16,000+ fraud signals,
and its unique ability to detect and prevent fraud in real
Data breaches at major airlines have been in the news a lot lately,
highlighting the increasing supply of basic payment data in the
black market economy. British Airways, Air Canada, and Cathay
Pacific all lost millions of clients’ credit card numbers, email
addresses, passport numbers, and more, which will probably
be used in attempts to defraud other airlines and travel industry
merchants. What hasn’t changed, worryingly, is that many large
airlines still rely on basic fraud checks that can easily be by passed
by 21st-century fraudsters and have yet to implement more ad -
van ced fraud prevention solutions based on richer data sets not
yet compromised by these fraudsters.
Many airlines today rely on legacy infrastructure and anti-fraud
solutions based on technology developed in the ‘80s and ‘90s,
such as address verication services (AVS) and card verication
numbers (CVN). A quick look at the data available on the open
web and in dark web marketplaces would quickly reveal to any
payments executive that these identiers are compromised and
can be bought cheaply and in bulk by fraudsters.
Get better data
What are the airlines missing? Today, there are many more
classes of data that can be used to authenticate transactions.
This includes biometric data, behavioural data, and device iden-
tity data. Now, you can determine if a person is who they say
they are by authenticating their voice, their thumbprint, or their
eye scan. If you are trying to minimise friction in your check out
process, you can authenticate a customer by how they interact
with your webpage and/or their device – a technology that is
becoming increasingly popular, especially with banks.
You can also add the use of device identity data in fraud preven-
tion, which is becoming commonplace enough that some provi-
ders of traditional personally identiable information (PII) now
supply device ID data in their solution oerings as well.
There are dozens and dozens of fraud solution vendors that enable
merchants to seamlessly incorporate these new data types into
their payment ow. AboutFraud.com regularly updates a list of
these vendors, ltered by solution type, so merchants should
have no trouble locating them. Unlike older fraud prevention tools
like AVS, airlines also need not worry about the geographical
limits of these solutions. There are at least a couple of solutions
active in every major geographic market and all the new data
types and the technology they leverage are truly global in nature.
Fully beneting from automated risk scoring
Airlines should not be late adopters to advanced fraud prevention
technology. Their business model leaves them more exposed to
fraud than the typical merchant. Currently, airlines use a number
of dierent sales channels, including their websites, online travel
agencies, consolidators, and travel agents, but many still only
apply one uniform set of antifraud rules across these very die
rent channels. Moreover, airline customers come in every shape
and form, from locations all over the world. Some customers still
plan their trips months in advance, but the entire travel industry
is experiencing growing volumes of last-minute purchases by
both business travellers and tourists. This makes it very dicult
to create a clear rules set that will block the fraudsters without
losing a signicant number of legitimate purchases.
Airlines Need Better Anti-Fraud Data
Ronald Praetsch | Co-Founder and Managing Director | About-Fraud.com
About Ronald Praetsch: Ronald Praetsch is Co-Founder and Managing Director of about-fraud.com. He also
consults regularly with merchants, payment service providers, and fraud solution vendors. Before founding
about-fraud.com, Ronald spent close to a decade in various payments and fraud prevention roles at Sift
Science, Fareportal, Booking.com, and Pay.ON, in both Europe and North America.
About About-Fraud.com: About-Fraud.com delivers
expert knowledge on technology and trends to a global
community of a fraud fighting professionals. Fraud
mana gement is super complex, with online businesses
struggling to understand and keep pace with evolving
trends, technology, best practices and providers.
To these businesses About-Fraud.com provides market
research and consulting services.
Unsurprisingly, a recent CyberSource study found that airlines
still need to manually review 18% of orders, despite only 12%
of manually reviewed bookings ultimately being cancelled. While
this represents a signicant improvement over 27% of trans
actions were manually reviewed in 2014 –, it is still too high.
Bringing down the manual review numbers even further would
require not just increased automation but smarter automation, ie
articial intelligence solutions fed with enough meaningful data
points that they can make decisions not only faster, but also
better than the typical fraud analyst.
Data is the lifeblood of fraud prevention
A handful of major platforms have enabled airlines to bring down
their manual review rate and adapt to changing and complex
fraud trends with automated risk scoring engines that utilise
machine learning models to predict transaction risk. But even
the most advanced machine learning algorithms won’t solve the
problem of ‘garbage in, garbage out’. Put simply, to dramatically
reduce fraud and false positive rates these systems need large
amounts of data that can be used to distinguish customer iden-
tity and risky transactions.
To cut down on revenue lost to inecient fraud prevention mecha
nism, airlines need to spend more time and resources on testing
the ecacy of new data types for preventing fraud across dierent
sales channels. The big banks are doing it. Apple and Microsoft
are doing it. It’s about time the airline started doing this seriously
as well.
The telecom world is changing, all while enabling the digitisation
of services across dierent sectors; these changes, however, are
increasing the fraud risks and threats within the telecom world
itself. Due to digitalisation, telecom services can be both the point
of attack to initiate fraud, as well as the victim of fraud.
As the phone has become a common authentication point for
many financial or ecommerce services, fraud against telecom
consu mers and the telecom services is rising rapidly. Further
increases in fraud impacts are due to the inherent value of
the equipment being supplied by telecom providers, thus
becoming attractive targets for the criminal fraternity, as they are
items that can be quickly cycled to revenue.
The rise in consumer-based fraud attacks against telecom services
is highlighted within the last Communications Fraud Control Asso-
ciation fraud report, which showed a combined value of over USD
11 billion lost to various types of consumer-related attacks, and
even this number is thought to be highly underestimated.
Recent years have seen a re-growth in subscription fraud attacks,
in order to gain equipment and services. As well as over 300%
growth in account takeover attacks in order to compromise the
consumer themselves, particularly in relation to nancial services.
Although subscription fraud has been a perennial problem for
almost all service industries, recent growth has been focused
around the use of “credit mules” or synthetic identities.
“Mules” are when a genuine entity has been approached and
knowingly passes on their personal details in order to allow
them to be used for an account creation. These mules are often
recompensed immediately and do not necessarily realise that
the details they provide will be used for a fraud attack and may
damage their future credit prole. As such, it is dicult for service
providers to identify mules as the details being used to create an
account are genuine and not falsied.
A synthetic identity is when an identity and a credit profile are
created by combining both genuine and fake data in order to
set up accounts across multiple services – services which may
be very low value, but with small credit interactions. This can
then create an impression of a credit active consumer, so when
the synthetic ID is used for a major purchase, the credit le and
history are apparent and warning ags may not be raised.
The growth of online and ecommerce channels allows the use of
“mules” and synthetic identities in high volumes remotely, thus
ena bling attackers to manipulate thousands of transactions over
a short period of time.
Account takeover has been the fastest growing form of fraud
for telecoms over the past few years. Much of this is attributable
to the changing nature of service provision. Customers now expect
instant access to accounts, simplied services, and recognition of
loyalty. As such, often accessing and adding services or equip ment
to existing accounts is faster and simpler, with fewer checks and
verications than opening new accounts. Fraud operatives have
targeted such principles ruthlessly.
Communications Fraud Control Association
Telecom Fraud The Impact of Digitalisation
Jason Lane-Sellers | President & Director | Communications Fraud Control Association
About Jason Lane-Sellers: Jason is a highly experienced fraud professional who has been working in
the telecommunications industry for 20+ years, and he is currently President of the Communications
Fraud Control Association. He has a wealth of experience within operators and vendors covering fraud,
risk & revenue assurance.
Click here for the company profile
About Communications Fraud Control Association:
CFCA is a not-for-prot global educational association
that is working to combat communications fraud.
The mission of the CFCA is to be the premier inter-
national association for revenue assurance, loss
prevention and fraud control through education and
information. By promoting a close association among
telecommunications fraud security personnel, CFCA
serves as a forum and clearinghouse of information
pertaining to the fraudulent use of communications
In the early stages of account takeover growth, a large focus
for fraudsters was on the ability to upgrade equipment or add
additional connections and equipment to existing accounts – if
you could access the customer account, you could perform these
actions to gain equipment for resale. However, the growth of
digital services across the dierent markets means that there are
other reasons to compromise a telecom account. Taking phone
numbers as a case in point, which are increasingly being used
as an authentication tool for ecommerce and online financial
services, whereby a message or call is sent to the phone/cell
to approve an ecommerce or nancial transaction. Fraudsters
have now realised that if they perform a SIM swap, or even port
out a telecom account service, they can then gain the ability
to not only add services to the telephone account, but also
use the phone number to intercept and approve financial
transactions, compromising both the victim’s nancial services
and their telephone account. This proves doubly damaging for
the telephone provider, as they are seen as responsible for the
attack against the nancial transaction, as well as for the phone
Now, most of the growth in these types of fraud has been driven
via the digitalisation of services and provision of apps, online
self-service, and digital interactions. Therefore, from a fraud
mana gement point of view, in order to start to manage or prevent
many of these types of attack, it is necessary to understand the
nature of the customer and digital services. As these attacks
are manipulated across dierent marketplaces, it can be dicult
for traditional service providers to adapt.
Therefore, telecom providers need to be able to understand
and identify their customers in the new digital world. As we
move into the crossover between service provision, access, and
utilisation, where people interact with multiple devices, in multiple
locations and across services, it has never been more important
to be able to prole an entity as a complete digital persona.
Leading organisations in the telecom world are now integrating
digital identity solutions in order to protect their customers, authen -
ticate interactions, and prevent fraud attacks.
The most advanced solutions amongst these allow the crowd-
sourcing of data across verticals, for a complete digital picture.
These solutions enable the provider to openly promote the use
of online services, whilst validating data and ensuring trust in
interactions across their digital channels. Operators who are not
following this trend or approach are quickly becoming the targets
of the advanced criminals to a frightening scale.
With ever more finance and ecommerce apps present on our
smart phones, SIM swap fraud is a lucrative choice for fraudsters
looking to gain access to victim accounts, credit cards, and
personal data. Online account providers, from social media to
ecommerce and banks, frequently encourage users to add a
mobile phone number as part of their “two-factor authentication”
strategy in order to secure their users’ account access or before
allowing users to carry out financial transactions. The mobile
phone number linked to the user account is then used to validate
that future attempts to access services are made by the genuine
customer. But what if a third party has managed to gain control
of this number?
SIM swap fraud is largely made possible due to the fact that
customers are able to switch SIMs while carrying their current
phone number with them. Fraudsters exploit this possibility,
calling network operators and posing as the victim claiming to
have lost their SIM card or needing switch to a new provider.
If the fraudster successfully passes the security questions asked
by the operator, they will be able to transfer the victim’s phone
number over to a SIM card in their control.
As additional personal information about the victim is required in
order to complete this kind of attack, SIM swap fraud is frequently
the second stage in a wider fraud attack usually starting with
targeted social engineering. Potential victims are identied and
targeted with phishing emails or calls seeking to discover personal
data including passwords and secret answers.
Victims often struggle to tell the dierence between these highly
personalised and sophisticated requests for information against
legitimate communications from their bank or websites they
frequently use. Key information such as full names and dates
of birth can also be gained by searching social media or other
public websites allowing a potential fraudster to quickly complete
a prole of their intended victim or victims. This research stage
of the attack will often help the fraudster discover which banks
or ecommerce sites are used by the victim, and so the fraudster
will know which companies to target once the SIM swap stage of
the fraud has been successfully carried out.
Once the fraudster has control of their victim’s phone number,
relatively unlimited access is available to any of the victim’s
accounts that use SMS messaging as the second factor for
authentication. Security texts will be sent to the number now
in the fraudster’s control, locking the victim out of their phone
and their accounts. When successfully combined with social
engineering, SIM swap fraud can lead to the equivalent of a
“device takeover” attack as the victim’s Apple account, for
example, can be set up on a new iPhone in the fraudster’s
control. This is made possible as long as the fraudster possesses
all of the vital security answers which will have been gathered
during the social engineering stage of the attack and may allow
the fraudster to go as far as adding a new ngerprint ID to the
victim’s Apple account. At this stage, all of the victim’s iPhone
apps, and therefore nancial data stored within those apps, are
in the fraudster’s hands.
Emma Mohan-Satta
Sim Swap Fraud an Attack in Multiple Stages
Emma Mohan-Satta | Senior Fraud Manager
About Emma Mohan-Satta: Emma has been working in fraud prevention for the past decade developing
knowledge across nancial services and ecommerce. After working for American Express, she gained
experience with a number of fraud prevention vendors and now looks after fraud risk and strategy for a
ntech startup called Capital on Tap.
While the victim is likely to detect the issue relatively quickly
when access is lost to their phone number and device settings,
putting it right and regaining control of their identity can prove a
time-consuming problem while operators and account providers
seek to conrm the true identity of the customer. This additional
time allows the fraudster to complete their attack and drain the
victim’s accounts or gain further personal data for carrying out
future attacks such as setting up new fake nancial accounts in
the victim’s identity.
Online account providers, particularly in the nancial services
industry, can look for risk indicators such as a change in device
behaviour to identify a change in identity behind the account
access. This may lead to taking additional precautionary and
verication steps before sending a secondfactor text message
to a number under the control of a fraudster. Providers may also
wish to consider the use of app-based authentication where the
device itself, rather than the phone number, forms part of the
authentication. When a significant change in device or device
settings is detected, additional steps can be taken before sending
the authentication code to prevent a fraudster from intercepting
this valuable code.
Users can also limit the potential for their own accounts being
caught in such an attack by limiting the amount of information
they reveal about themselves online and exercising caution
when receiving emails or calls purporting to be from their bank.
By avoiding the social engineering stage of the attack, the poten-
tial for a fraudster to carry out a SIM swap is greatly reduced.
Victims may also become aware that they have become the victim
of SIM swap fraud when they lose phone signal and so should be
advised to contact their phone operator immediately if this occurs
unexpectedly without regaining signal soon after.
While the increased use of two-factor authentication continues
to help in the ght against online fraud, companies should be
aware of the potential to exploit the frequently-used SMS second
factor. Businesses should continue building layered strategies
and using technology to identify suspicious account activity and
fraud risk to avoid an over-reliance on SMS security codes in
customer authentication.
What are the main types of fraud in the online
gaming industry and what transaction types are the
most aected?
As the gaming industry becomes increasingly digital, it becomes
exponentially exposed, especially at a transactional level. While
the videogame consumer population is particularly aware of grey
markets and tricks, fraudulent channels of retail are easy to put in
place. Wellinformed nal customers just need to give the fraud
ster their player account credentials so the fraudster can process
the transaction on their behalf with a stolen payment method.
All of these points make the fraud on gaming products attractive to
fraudsters. Immediate consumable digital contents, like in-game
currency, are the most popular products among fraudsters.
In that case, it is not only about the nancial impact, but this
situation also brings inequity between players who can aord to
buy extra content to be more competitive and those who can’t
or don’t.
Given the international coverage, what insights can
you share with us regarding fraud across dierent
Because most of the defrauded products are digital content,
the underground videogames market is global.
It is very important to be able to display a consistent product
pricing list all over the world as well as it is important to be able
to properly identify the customer’s country. This way you will avoid
customers from strong currency countries buying on softer
currency countries.
In terms of fraud detection, the most important is to have a
consistent payment method strategy for each geographical area.
Then you should be aware of all the specicities related to the
main payment methods. Is it easy to do a chargeback? What is
required to open the payment account? How does the payer log
in his account?
For example, 3-D Secure in Europe is reliable, while the charge-
back process is easier in North America. Some countries tend
to use payment methods that can be more trustworthy because
they need more authentication clearances during the account
creation process, or during the transaction step itself.
Take all of these specicities and build a tailored fraud strategy
according to each area.
Sithy Phoutchanthavongsa | Fraud expert | Ubisoft
About Sithy Phoutchanthavongsa: Sithy is the fraud expert at Ubisoft. He has 10 years of experience in
fraud detection and prevention strategy performed within banking and ecommerce sectors, rst as part of
the business teams and then as a fraud service provider. He joined the Ubisoft ecommerce team in 2016.
His mission is to dene Ubisoft’s fraud strategy and to dig out and respond to any risk related topics.
Sithy Phoutchanthavongsa, Ubisoft’s fraud expert on the status of online gaming industry fraud, with insights into the grey
The best tools for fraud
detection would never be
complete without both a good
knowledge of players and a
consistent external/customer
We encourage fraud prevention experts to share
their knowledge with their peers in order to bring a
positive impact on the online business environment.
Therefore, what advice can you give to other mer-
chants so they can keep their business secure and
their customers loyal?
Ubisoft’s ambition is to maintain a direct and active channel
with players. The best tools for fraud detection would never
be complete without both a good knowledge of players and a
consistent external/customer communication. The benets of fair
play between players, the importance of securing their accounts,
and not buying from unauthorised resellers, the reasons for
limiting friendly fraud behaviors… All of the above should be
brought to players’ awareness in an educative and appropriate
way. This combination is key for fraud mitigation success.
What are the best fraud prevention strategies for
securing both the online gaming platforms and the
consumers’ data? Is there any particular authen-
tication method that you recommend?
During the real-time scoring, it is important to couple both a wide
enough metrics panel and the knowledge you have on the player.
We consider that whatever metrics say about the customer
during the transaction, it always has to be contextualised by the
data on players’ habits, stats, history.
Reactivity is also key and has to be optimum; because digital
trans actions are instant delivery, it is important to put in place
dynamic tools and rules that can be updated very quickly, such
as with machine learning systems.
As for the player’s data protection, Ubisoft takes the GDPR rules
very seriously. We have a dedicated team in place to help apply
it everywhere it is needed, every step of the way, and maintain
our policy up to date.
When it comes to authentication, any type of two-factor authenti-
cation is recommended, whether by mobile or email. On top of
regular transaction authentications, education is key. Providing
players with all the necessary information to understand why and
how to protect their account can help change their habits.
How are you dealing with false positives and false
negatives? What challenges do you encounter in this
Obviously, using relevant analytics tools and defining and
monitoring the appropriate metrics helps. Yet, the importance
of communication and collaboration with teams outside of the
fraud department should not be underestimated: customer
service or business operational teams can denitely help reduce
false positives on the condition to build an ecient channel of
knowledge sharing and information escalation. This is a great
way to reduce friction generated by false judgement.
At Ubisoft, our main challenge is that, with over 14,000 employees
located in more than 30 countries, we need to keep everybody
on the same page and streamline feedback collection.
About Ubisoft: Ubisoft is a leading creator, publisher,
and distributor of interactive entertainment and services,
with a rich portfolio of world-renowned brands, including
Assassin’s Creed, Just Dance, Tom Clancy’s video game
series, Rayman, Far Cry and Watch Dogs. The teams
throughout Ubisoft’s worldwide network of studios and
business offices are committed to delivering original
and memorable gaming experiences across all popular
platforms, including consoles, mobile phones, tablets
and PCs.
When implementing a fraud strategy for each Richemont brand,
the key is to ensure we provide an ecient and seamless veri
cation process. This rules out the possibility of using any verica
tion method that may cause delay to the shipment’s order or incon-
venience to the client. As less than 30% of Richemont ecommerce
orders are placed by returning clients, good customer service plays
a crucial role in the way we handle orders placed predominantly by
new clients.
Fraud challenges at Richemont
As an online luxury retailer, we face many challenges with fraud
management. At Richemont, we experience vast volumes of card
testing fraud in Italy and France; in the UK, we see an emergence
of 1st party fraud and account takeovers. However, our biggest
challenge for the Richemont Fraud & Payments team is fraud on
MOTO orders.
MOTO, an acronym for Mail Order/Telephone Order, represents
50% of the transaction split for Richemont. Due to the value of
the products sold within Richemont brands, we nd that the client
usually prefers to speak to a brand specialist before deciding on
the purchase. Unfortunately for us, the MOTO channel is also
preferred by fraudsters.
Since we introduced 3-D Secure in 2017, we have seen a change in
the fraudster’s behaviour. As illustrated in the chargeback ana lysis,
we have seen the fraudsters drastically shifting from targeting the
websites to targeting the MOTO channel and placing an order via
Customer Services.
With Low Order Volumes, Richemont Faces a Dierent Fraud Review Challenge
Leon Brown | Fraud & Payments Manager | Richemont
About Leon Brown: Leon Brown is the Fraud & Payments Manager for Richemont. Leon is managing
the Fraud & Payments for all ecommerce Maisons, operating under the Richemont umbrella. With nearly
ten years of experience in Fraud & Payments, Leon’s previous experience includes Selfridges and Net-
About Richemont: Richemont owns several of the
world’s leading companies in the eld of luxury goods,
with particular strengths in jewellery, watches, and
writing instruments. Our Maisons encompass several
of the most prestigious names in the luxury industry
including Cartier, Van Cleef & Arpels, IWC Schahausen,
Jaeger-LeCoultre, Ocine Panerai, Piaget, Vacheron
Constantin, Montblanc, Alfred Dunhill, and Chloé.
What is a MOTO order and why is it the preferred
target of the fraudsters
Mail order telephone order is when clients decide to contact the
Customer Relations centre, as they want to place an order over
the phone, instead of using the website. There are several reasons
for why this happens. The main reason is that many of our clients
prefer the experience of speaking to a trained brand expert for
reassu rance before making such a huge investment. Another rea-
son is that many of our clients may experience problems placing
an order via the website due to the widespread issue we have with
card issuers declining high-value transactions under the “do not
honour” reason code. Once the client is put through to a brand
specialist, their order is placed by the specialist using an internal
version of the website. Once the order is complete, the client will
receive conrmation of their order via email.
The reason a fraudster prefers to place an order via the MOTO
channel is a simple one: lack of security.
On the website, we are protected by 3-D secure in most cases, and
for the boutiques, we have chip & pin. For MOTO, we have none
of the security features mentioned above. To place a MOTO order,
you need an address, a card number, expiry date, and the CV2.
In the UK, the US or Canada, we sometimes have the AVS for
reassu rance; but what happens when we have a high-value MOTO
order from France or Italy, where AVS is exempt, with a billing
and shipping address mismatch?
Rejecting an order, just because there is no AVS or because there
is a mismatch with the billing and shipping, is not an option.
Dealing with the risk of MOTO at Richemont
Although 76% of chargebacks received is through the MOTO
channel, the fraud and chargeback rate for Richemont is still com-
fortably below the acceptable industry average. Here are a few tips
we use to manage fraud on MOTO orders.
Fraud tools. It’s essential to research and invest in tools that can
help you with order verication. In particular, invest in tools that
can help you with address, email, and phone number verication.
Since the implementation of several fraud tools, we have drasti-
cally reduced fraud in key markets like the UK.
Verication question. It’s always tricky when you have to remem-
ber a lie. Based on our experience, this is usually the case with
fraudsters. If we highly suspect a MOTO order, there is no harm
in calling the client to verify a few order details. What we nd in
most fraud cases is a hesitance or reluctance to conrm certain
aspects of the order. For example, the fraudster can verify the
shipping address but struggles to confirm the billing address.
It’s crucial that you understand your typical client and use this as
a benchmark when speaking to a potential fraudster to identify
discrepancies in their behavior, the tone of voice.
Feedback. Speak to your Customer Service department and ask
for feedback. How long did the client spend selecting the product,
compared to your typical client? If the item he requested was out
of stock, was the client specic with their back up option, or was
the client just eager to complete the transaction?
Visit www.merchantpaymentsecosystem.com for more information
February 19-21, 2019 Berlin Available until December 31
Want to hear & network
with the industrys top
minds about:
Here are just a few you'll hear from
Steve Cook
Specialist Biometrics and
Fintech Consultant
Kelsey Blakely
Fraud Risk Operations Lead
Martin Sweeney Bartosz Skwarczek
CEO and Founder
Kieran Cotter
Fraud Risk Manager
Rahul Pangam
CEO & Co-Founder
Emilie Grunzweig
Head of Marketing Analyst
... and many more to
be revealed soon !
paypers nov A4v1.pdf 1 20/11/2018 16:18
Best Practices of Mitigating Fraud in Banking
Mirela Ciobanu | Senior Editor | The Paypers
The nancial services industry continues to position itself in a juggling position, with banks and nancial services insti tutions
facing multiple challenges tied to regulations, legacy systems, disruptive models and technologies, new competitors, and
a highly demanding customer base, while pursuing new strategies for sustainable growth.
For 2018, banks have had to deal with managing their digital channels and threats associated with their use, such as new
account opening and account takeover, implementing the Open Banking and Instant Payments initiatives, bringing to life
the ultimate digital banking experience, adopting cloud services and data analytics, all frosted with the increased threat
posed by fraudsters that are getting more and more sophisticated.
As the threat environment continues to escalate, eective fraud prevention has become an increasingly competitive
issue for FIs. According to a research conducted by iovation and Aite, the most challenging fraud cases for FIs are
sophisticated card fraud, application fraud, account takeover (ATO) attacks, wholesale ATO, and the spectre of faster
Fraudsters getting more sophisticated
Despite eorts to control payments fraud, it appears nancial institutions and businesses across the globe are ghting a
losing battle. A TransUnion study has revealed that 94% of nancial services have experienced fraud within the last two
years, such as identity theft, synthetic identity fraud, or account takeover.
In addition, the European Payments Council (EPC) issues a yearly report on trends in security threats that aect the
pay ments landscape. In its most recent report, from December 2017, the organisation identied the main payments
threats, some of which we will try to cover briey in our article:
- a greater degree of professionalism of cybercriminals shown by the organisation and sophistication of recent cyber-
- the number of DDoS attacks is on the rise, with bad actors frequently targeting the nancial sector;
- the attack focus has shifted from malware to social engineering attacks;
- botnets still remain a signicant attack vector, and because of the high volume of infected consumer devices (eg PCs,
mobile devices, etc) severe threats remain;
- mobile devices and IoT devices are becoming an attractive target for cyber criminals;
- the adoption of cloud services together with big data analytics technologies, which results in data stored ‘everywhere’,
are bringing new opportunities to businesses, but new risks as well.
The nancial services industry has always been heavily regulated, and security and fraud prevention mechanisms are
gene rally stronger than in other industries. Nevertheless, fraudsters see ntech providers as easier targets than traditional
nancial services companies as they attempt make use of new and emerging platforms to exploit gaps in process and
infra structure.
Fraud Mitigation - Key Challenges for Banks
Some of the reasons behind this vulnerability could be that ntech companies do not necessarily have the resources such
as skills and funds to implement sophisticated fraud defence/detection mechanism. According to JAX Finance speaker
Rona Ruthen, ntechs are especially vulnerable, as in the early days the team is very lean, and the focus is on developing
the product/systems and nding the productmarket t. Fraudsters know that, so they target ntech companies early on,
and adapt very quickly to changes in controls.
Nevertheless, big nancial services companies’ customers are also targeted, despite having strong defences. One of
the most eective ways of defrauding customers is to lure them into complex social engineering scams that result in a
genuine customer unwittingly transferring funds to a mule account, or even allowing direct account access.
These attacks/attempts can take place across many channels, including email, SMS, calls, and social media channels,
as any communication channel used to communicate with customers and users can be exploited by an attacker, with
varying degrees of sophistication required to carry out the attack. All types of social engineering attacks continue to be
used by attackers of varying levels of capabilities, with particular increase in Business Email Compromise emails and
phishing emails that result in malware being deployed on computers.
To ght them, nancial institutions are advised to put the appropriate transaction ltering and monitoring systems
in place and use customer proling to detect suspicious payment transactions. However, a very important aspect to
counter the social engineering attacks is continued awareness raising campaigns.
Another big threat in nancial services comes from device spoong, as fraudsters attempt to trick banks into thinking that
multiple fraudulent login attempts are coming from new customer devices, perhaps by repeatedly wiping cookies or using
virtual machines.
Regarding the mule accounts, mule networks continue to negatively impact the global banking ecosystem, according to
the ThreatMetrix Q2 2018 Cybercrime Report. Money mules are people who serve as intermediaries for criminals and
criminal organisations. Whether or not they are aware of it, they transport fraudulently gained money to fraudsters. Thus,
the use of intermediaries makes it dicult to gure out the identity of the fraudster. The challenge for nancial insti tutions
is how to detect mule activity when individual account behaviour may not trigger highrisk ags. To ght it, organisations
need to create mule watchlists, and build machine learning models to identify new mule networks based on existing risk
Oering the ultimate digital banking experience
Current onboarding processes are seen as time-consuming, costly, and as if they deliver a poor customer experience.
However, when trying to innovate and oer great and frictionless customer journey while banking, nancial services
institutions are struggling to balance this experience with security threats.
Fraud Mitigation - Key Challenges for Banks
Among these threats, on top of the list are account takeover and new account applications. Account takeover is a
form of identity theft. This type of fraud doesn’t necessarily have to start with what is traditionally considered highly
sen si tive information, such as a social security number or PIN. According to Chargebacks 911, account takeover can
potentially be started from nearly any scrap of personal data: an email address, a full name, a date of birth – any identier
entered during the validation process can work. Historically speaking, banks and card providers have been the main
targets of account takeover fraudsters.
Application fraud has become an increasing issue for organisations in industries such as banking, credit card appli-
cations, instant store credit, and retail, to name a few. Some of the reasons behind the rise of this type of fraud might
be the large volume of personally identiable information (PII) available on the black market for fraudsters to use, the
abandonment of stringent manual application review processes by nancial institutions and merchants when customers
open new accounts, and fraudsters using stolen identity data combined with bots to open accounts at a very fast rate.
To prevent these types of fraud, nancial institutions are advised to close the door on fraudsters before they can gain
access to any account opening processes. InAuth security experts advise businesses to watch for bot attacks since they
are capable of opening hundreds of accounts in a short amount of time, with bad actors often using the same device
repeatedly to perform the fraudulent transaction until the device is detected and disabled.
Thus, device authentication is also an important way to thwart fraudulent account opening, as it enables organisations
to verify the identity of a device by the device’s unique characteristics. Moreover, a device riskiness assessment is needed
to validate whether an additional review is necessary for the account opening process, such as bot detection, spoong
tool detection, malware detection, and the ability to use negative lists for devices associated to fraud.
Coping with Open Banking
Under PSD2 banks must open up their systems to authorised thirdparty nancial service providers (TPPs) to
enable these companies initiate and process payments and nancial transactions at the request of the bank’s
customers. However, these requirements are a source of concern for many banks, as this access is not without risk.
According to OneSpan, formerly Vasco, the most important security and privacy threats against the APIs provided by
banks to TPPs include:
- API vulnerabilities, resulting in injection attack causing dump of personal information of bank’s users;
compromised or malicious TPP leaking nancial information obtained from bank;
- API vulnerability leading to man-in-the-middle attack manipulating transaction data;
- compromised or malicious TPP issuing fraudulent transaction request;
 ooding of API aecting quality of service for users;
- compromised or malicious TPP locking out users with invalid authentication requests.
To overcome these threats, banks are advised to use transaction risk analysis to detect fraudulent transactions and user
behaviour, choose a suitable authentication model for their users, protect the communication channel with TPPs, detect
and prevent API implementation vulnerabilities and security incidents at TPPs.
Fraud Mitigation - Key Challenges for Banks
Another key aspect in the context of Open Banking is consent that needs to be explicit, as mandated in PSD2 in
accordance with the GDPR. Banks have to allow customer info to be shared, but only if that user explicitly gives per-
mission to the new provider. However, third-party access to customer accounts and the associated data will inevitably
raise concerns about security and privacy. Consequently, privacy, consent, and fraud detection tools will become
crucial to customer engagement and building in trust.
As explained by Mike Nathan, ThreatMetrix, in the Open Banking Report 2018, banks must ensure the same level of
security across all access points including the Open Banking environment, with the additional check around consent.
They also must focus on risk control and put more emphasis on active risk management and monitoring.
Instant payments adoption
November 2017 saw the launch of the SEPA Instant Credit Transfer (SCT Inst) scheme, an initiative aimed at easier
and faster payments on a pan-European scale. Among the features, the most relevant one is immediacy – when the
funds are available in less than ten seconds after the transfer is initiated. One cannot omit benets such as meeting the
demand of customers for great payment experiences and replacing paper-based payment instruments, such as cash and
cheques. However, this initiative has also left payments facilitators facing problems such as ‘instant fraud’, with banks
having to adopt operational and risk management processes such as fraud detection to spot fraudulent transactions.
In this context, in the case of authorised push payments fraud it is hard to claim the amount of money back as funds
are transferred instantly. And this is a rising concern; for instance the trade body UK Finance announced that businesses
and consumers lost GBP 236 million in 2017 through authorised push payment (APP) frauds. APP frauds take place
where a victim is conned into authorising a transfer of money from their bank account into an account, which they believe
is controlled by a legitimate payee, but is actually controlled by a fraudster.
In order to avoid APP scams, educating consumers and business towards being more alert when making electronic
money transfers is crucial. Internet users are advised to never disclose security details, such as their PIN or banking
password, and should never assume an email, text, or phone call is authentic. Never rush a payment, as ‘a genuine
organisation won’t mind waiting’, says the trade association, which adds that ‘listening to your instincts’ and ‘not
panicking’ are essential if something does go wrong.
Adoption of cloud services and data analytics
Cloud services are resources made available to users on demand via the Internet. They are oered by cloud computing
provider servers as opposed to being provided by a company’s on-premises servers. As organisations continue to migrate
onpremises services and applications to the cloud, we can deduce that they will also suer the same fraud threats and
risk, with the addition of new ones. Weak code and software vulnerabilities in the cloud, outside the traditional perimeter
of control, may produce dierent types of breaches and fraud.
To prevent these issues, the European Payments Council (EPC) recommends cloud providers to have a clear set
of policies and cloud governance throughout the whole lifecycle of applications and services. Moreover, the architec
ture, applications, process, systems, and data in the cloud need to be desegregated from each other to avoid
propagation of malware or breach attacks.
Fraud Mitigation - Key Challenges for Banks
Last but not least, usage of new tools and applications for cloud computing and big data need to be analysed and
assessed from the point of view of security, risk, and governance, as some tools might not be suciently mature to use
and could potentially cause data breaches and fraud. Therefore, companies tapping into cloud services are advised to
conduct a thorough analysis from the security and fraud perspective before making any usage or buy decision.
In our digital world driven by a mobilerst customer mentality, many nancial institutions (FIs) have started to recognise
and act towards satisfying the need for an omnichannel experience for their customers. But this task can became dicult
as they need to determine with 99.99% accuracy the identity of the person on the other side of the computer or device,
consider real-time fraud threats and real-time fraud solutions, while staying competitive and compliant. Fortunately, the
digitization of banking services brings new technological solutions able to tackle modern security challenges and detect
suspicious behaviour eciently, helping nancial institution services to protect digital data from fraud.
Fraud Mitigation - Key Challenges for Banks
The fight against financial crime is changing and banks are
struggling to keep up. Financial institutions are already losing
ground in the adoption of open banking initiatives like PSD2.
Coupled with the increasing market demands for compliance and
transparency brought on by regulations like the GDPR, it’s clear
that banks have a lot to deal with. The nancial industry is quickly
shifting towards realtime payments and instant services,
two key aspects of a frictionless customer experience. However,
these frameworks present serious challenges to the security
side of things particularly where nancial crime is concerned.
At the same time, fraud schemes are growing more complex.
For example, according to Javelin, “criminals are opening more
new accounts as a means of compromising accounts consumers
already have.” And when it comes to money laundering, schemes
now go beyond trafficking, with successfully laundered funds
often being linked to bribery, inuence peddling, corporate crime,
or political intrigue. To protect their reputation and the trust
they’ve built with their customers, banks need to look beyond
their existing nancial crime prevention strategies and discover
how they can better address the world of real-time payments.
Three breakthroughs in fraud management
Over the last year, Feedzai has integrated three key features into
its AI platform to help banks meet the growing challenge of real-
time fraud prevention. Whether used separately or in tandem,
these tools oer powerful new ways to stop fraud in its tracks.
A primary drawback of many modern fraud detection systems is
that they force users to operate within constructs that don’t make
sense for their enterprises. Until now, users were left with one of
two choices:
Work within inecient data science environments oered by a
Rely on their own (and often legacy) fraud management plat
forms that lack modern machine learning algorithms.
Feedzai understands that this is an impossible choice and oers a
third door: Open Machine Learning (OpenML). Known colloquially
as “bring your own machine learning,” Feedzai’s OpenML
Engine is a machine learning environment that lets users integrate
their own machine learning tools, libraries, algorithms, and models
into the system. In essence, it gives users access to a powerful
fraud management platform while still allowing customization to
the user’s specic needs. The OpenML Engine includes an SDK
for Python, R, and Java, while also providing close integration with
machine learning tools like H20, R Studio, and DataRobot. It’s a
revolutionary integration that gives your fraud detection system
the benets of a purposebuilt platform while letting you retain
access to the open source libraries used by your own company.
From a customizable fraud management perspective, there’s
nothing better.
AutoML is Feedzai’s way of accelerating the machine learning
process and increasing the speed at which banks are able to
confront new fraud threats.
Machine Learning Innovations for Fighting Financial Crime in an Open Banking Era
Pedro Bizarro | Co-founder and Chief Science Ocer | Feedzai
About Pedro Bizarro: Pedro Bizarro is co-founder and Chief Science Officer at Feedzai. Pedro is a
researcher turned entrepreneur: after a 10-year research career (Computer Science PhD at the University
of Madison - Wisconsin, Fulbright Fellow, Marie Curie Fellow and winner of the BES Innovation National
Competition) Pedro is now CSO at Feedzai where he leads the Research team in developing the best
fraud prevention algorithms and tools. Pedro is a high performance data processing expert that loves data,
algorithms, visualization, and machine learning.
Click here for the company profile
About Feedzai: Feedzai is the market leader in ghting
fraud with AI. We’re coding the future of commerce
with today’s most advanced risk management platform
powered by big data and machine learning. Founded
and developed by data scientists and aerospace
engineers, Feedzai has one mission: to make banking
and commerce safe. The world’s largest banks,
processors, and retailers use Feedzai’s fraud prevention
and anti-money laundering products to manage risk
while improving customer experience.
Before, users had to manually execute many steps of model
development, including feature engineering, a very time consu-
ming task. AutoML changes the game by providing a completely
automated solution for model generation and develop ment, all
built into the Feedzai platform:
● Automatic feature engineering;
● Automatic model training;
● Automatic hyperparameter optimization;
● Automatic model selection.
Other AutoML platforms on the market (such as those oered by
Google) require substantial GPU capacity that most organizations
just don’t have. Feedzai’s approach works dierently, relying on
patent-pending, semantic-based automatic feature engineering
which significantly cuts down the needed processing power.
AutoML relies on a short and simple userdened conguration of
the semantics of each eld which is then used to produce features
automatically. Overall, this allows nancial institutions to quickly
iterate on many models and configurations very quickly with
minimal processing power. For example, complete proles can
be built around a single card, including the number of declined
transactions in a given time period, the distance between every
transaction location, the time between consecutive transaction for
each card user, and more. All of this is done through an automated
framework that requires minimal input from the data scientist,
reducing the classic data science workow timeline from eight
weeks to one day. Less time spent on model creation means more
time spent on data analysis.
Feedzai Genome is a powerful visualization tool that provides a
comprehensive, top-level view of transaction data. Where OpenML
and AutoML advance Feedzai’s data analysis capabilities, Genome
brings a visual perspective to the connections between nancial
transactions. Using a virtualization engine, Genome displays the
interconnected relationships between transactions and creates
a simple way to identify patterns throughout each data set.
Users can view the relationship between each transaction, view
transaction clusters around specific cards or users, and trace
the complete lifecycle of every transaction made—all within
Feedzai’s platform. This addition brings a new level of analysis to
Feedzai’s fraud detection capabilities. Images play into humans’
natural ability to spot patterns in visual data, and by taking a
visual approach to transaction review, users can instantly spot
the same patterns that may take fraud analysts weeks to recognize.
This goes beyond mere data analysis or risk scoring and creates a
new type of fraud detection system:
Oering deeper and more thorough assessments of the complete
nancial data set;
Enabling more ecient risk assessment, including deep insight
into the underlying relationships among each agged transaction;
Being purposebuilt to ght nancial crime and highlight suspic
ious fraud typologies.
A systemic view of instant payments fraud
These advancements speak to a growing trend in financial
crime detection: the need for nancial service providers to take
a system-wide view of interaction. From the registration of each
transaction to every customer touchpoint, true security comes
from complete, end-to-end assessments. The world of instant
payments is ripe with opportunity – yet if banks want to make
the most of these new frameworks, they’ll need to be prepared to
handle the challenges that will inevitably come.
Michael Lynch | Chief Strategy Ocer Je Wixted | Vice President of Product and Operations
InAuth Accertify
About Michael Lynch: Michael Lynch is
InAuth’s Chief Strategy Ocer and is
responsible for developing and leading
the company’s new products strategy,
as well as developing key US and
international partnerships. He brings
two decades of experience in key roles
within financial services, consulting,
and Fortune 500 companies, specia-
lising in security and technology lea-
der ship.
About Jeff Wixted : Jeff Wixted
oversees the global operations,
product strategy and roadmap,
and presales functions at Accertify.
Jeff brings over a decade of
experience in cardnotpresent
fraud and related use cases, he
also serves as the Treasurer on
the Merchant Risk Council Global
Fraud takes place in many forms and in many industries, and
has been rising in recent years. According to PwC’s Global
Economic Crime and Fraud Survey 2018, 49% of respondents
said their companies had suered fraud, up from 36% in 2016
an increase driven by rising global awareness of fraud, a more
robust response rate, and greater clarity around what ‘fraud’
actually means.
It is increasingly important to detect fraud at its earliest stage of
the nancial lifecycle, which, in many cases, is at the time of appli
cation for an account. Application fraud is a rapidly increasing
issue for organisations in industries such as banking, mortgages,
auto lending, nancial lending, credit card applications, instant
store credit, and retail, to name a few.
Credit card losses from accounts opened with fabricated identities
reached USD 820 million in 2017, up almost 17% from 2016. In
addition, Aite forecasts the losses to rise another 53%, to almost
USD 1.3 billion, by 2020.
What can companies do to mitigate application
fraud, particularly in digital channels?
The best way to prevent account opening fraud is to have robust
protections in place across the customer lifecycle and to close the
door on fraudsters before they can gain access to any account
opening processes. Device authentication is an important part of
thwarting fraudulent account opening, as it enables organisations
to verify the identity of a device by the device’s unique charac-
teristics. Device authentication technology uses unique attributes
in each device to create a device ID.
InAuth and Accertify
Accertify and InAuth: Fighting Fraudulent Account Opening
About InAuth and Accertify: InAuth delivers device
identification, risk detection, and analysis capabilities
possible to help organisations limit risk, remove friction,
and reduce fraud within their digital channels. Accertify, a
wholly-owned subsidiary of American Express, is a leading
provider of fraud prevention, chargeback management,
and payment gateway solutions to merchants’ customers
spanning diverse industries worldwide.
By creating and calling on this device ID for subsequent trans-
actions, organisations can authenticate trusted consumers with
the least amount of friction, providing a positive customer expe-
rience. Transactions from risky devices can be agged for next
level review or they can be denied altogether. If the same device
ID is opening many accounts in a short amount of time, this is
potentially a harmful bot. Another important tool in preventing
appli cation and account opening fraud is user behavioural
analytics. By quickly recognising typical from atypical behaviours
online, businesses can quickly identify potential fraud and prevent
it before it becomes a loss. Cybercriminals today use bots to
attempt to open several new accounts at once, by being able to
tell the dierence between a legitimate person attempting to open
an account and a bot, which is critical.
Solution: an end-to-end risk platform to thwart
account opening fraud
Accertify and InAuth are wholly owned subsidiaries of American
Express and have been working with the largest brands in the world
delivering fraud detection, with minimal customer insult so banks
and merchants can prevent fraud while growing their business.
By coupling InAuth’s device intelligence with Accertify’s risk
engine, behavioural analytics and machine learning, busi nesses
have unparalleled insights to thousands of device and trans-
action attributes – across all channels – to assess the riski ness
of an application and make a truly informed decision. InAuth per-
forms critical checks that could indicate that a fraudster may be
working behind the scenes and helps validate whether additional
review is necessary in the account opening process, such as bot
and malware detection, along with the ability to use negative lists
for devices associated to fraud. InAuth allows clients to associate
device elements with anonymised user data across multiple
industries, providing a holistic view of the trustworthiness of a
device, so that they can better assess the riskiness of a trans-
action and take additional steps to mitigate potential fraud.
In situations such as new account opening, any risk intelligence
of the device itself becomes critically important in order to make
more confident transaction decisions. InAuth provides critical
context, allowing businesses to expand their real-time defence
network and provide another layer of transparent authentication
that can be seamlessly incorporated into the account opening
process. Accertify’s portfolio of fraud management solutions
brings additional levels of control to identify and prevent account
takeovers and new account originations schemes.
By looking beyond the user-entered information and examining
anonymised site navigation data, customers are able to quickly
identify and stop complex fraud attacks such as identity theft,
bot trac, and automated attacks that might be missed by other
solutions. These behavioural analytics tools look at the speed
and manner in which customers interact with websites when
they complete their applications and establish usage patterns of
legitimate customers vs fraudsters.
Accertify’s solutions collect, store, and aggregate large volumes of
data in real time. Creating views around a customer, a product, an
event, or any number of data points can increase fraud detection
accuracy and reduce false positives. There is no shortage of fraud
prevention solutions on the market but it is important to partner
with those proven to deliver results. InAuth and Accertify work
with the largest global banks, merchants, and airlines and help
turn large volumes of disparate data into actionable intelligence
to help thwart online account opening fraud while protecting the
user experience.
Click here for the company profile
What are the current cybercrime trends in the retail and
corporate banking sector, particularly in the Nordic
We have divided cybercrime trends into local and global threats.
If we are looking at the global threats, which are likely to rise in
the coming year, we see investment scams, CEO fraud, Business
Email Compromise (BEC) fraud, phishing, smishing, and vishing.
Notably, vishing is prevalent in Sweden and it is likely to come
to Norway and other Nordic countries. At the local level, the
common threats identied are friendly fraud, identity theft, card
scams, and again phishing. Nevertheless, the employees are
usually the weak link, as in most cases the threat comes from
the inside. Why? Because the staff within the organisation is
not well trained to recognise a cyber-attack, or sometimes they
commit fraud on purpose. Due to the developed economy and
prosperous businesses, Nordic countries are highly digital, and
this makes them a good target for cybercriminals.
How does the anatomy of cyber-attacks look like?
There are two types of cyber-attacks; however, it is often some
kind of combination of the two: those where the fraudsters
manipulate people’s minds and those where the fraudsters
manipulate people’s devices (or hack/misuse email box,
inlogging, etc). The rst type is essentially the social engineering
fraud and it is usually exercised over an organisation’s staff.
Cybercriminals hack emails, but most of the time, at least for CEO
fraud, the manipulation of the employees is a common practice.
The attacks that go through social engineering are investment
scams, BEC fraud, love scams, phishing, smishing, vishing,
friendly fraud, and identity theft, but they can also include bits of
technical fraud.
The technical advanced fraud is when fraudsters have the skills
and knowledge of producing technical bits in order to attack, so
then they use malwares, dierent types of Trojans and viruses in
order to get into the computers of the customers. By any means,
the most successful frauds are those resulted from a combination
of social engineering and technical elements.
Could you please share with our readers some
re commen dations on strengthening the fraud pre-
ven tion management?
One of the important things to do, as an organisation, is to iden-
tify the risk group within. It’s not always about the money, the
information, or the dierent knowledge that only the company has;
the projects or any other type or valuable resources that can be
stolen and commercialised by fraudsters are also things worth
con si dering. It is also important to know what information is shared
between the company, the sta, and the public. In addition, one has
to always make sure that the employees are aware of the risks,
and they should always be updated about potential attacks.
Therefore, educating people on a constant basis is a way of
reducing risks. One should constantly monitor the way emails
are used (for instance, how the ags in the email function are
used), the money transfers, and other types of transactions.
When it comes to transactions, we recommend the four eyes
principle: two people to verify when the company made a
payment and to make sure fraudsters don’t manipulate the bills
or the emails. In addition, it’s always crucial to make sure the
utilised technology is up to date. And there is also the password
culture: obviously, people should understand they shouldn’t
share passwords under any circumstances, and they should
know how to build a strong password. Moreover, companies
should adapt a correct password culture for their sta.
The fraud management team of Nordea reveals key insights into the cybercrime trends and fraud management solution at both
local and global level.
By any means, the most
successful frauds are those
resulted from a combination
of social engineering and
technical elements.
About Nordea: Nordea is the largest bank by size in the
Nordic region and the only bank that has a truly Nordic
identity at its heart and culture. With key operations
in every Nordic country, Nordea has been playing a
fundamental part in establishing the shared economy
in the region and in fostering a borderless trading area.
Online Authentication - The Journey from
Passwords and Secret Questions to Zero
Factor Authentication
Mirela Ciobanu | Senior Editor | The Paypers
Traditionally, identity verication was based on human interactions and presenting physical documents, mainly issued
by governments. Still, as digital channels are becoming the go-to places where consumers interact with businesses and
each other, we cannot rely anymore only on those processes.
As a result, businesses have become incredibly dependent on technology to verify and authenticate identities in order to
give (new) customer access to a network of systems to manage, store, and transmit information such as nancial accounts,
personally identiable information, intellectual property, transaction records, etc. Within this web, identity verication, iden
tity validation and identity authentication verication have turned out to be central to the ability of these businesses to eec
tively secure access to consumer-facing digital channels and the systems that underpin their operations.
However, identity verication, identity validation and identity authentication represent three dierent types of checks/
digital transactions. As Trulioo mentions in a blog post, we need to build the necessary online framework of trust that can
conrm that the person actually exists, by checking the validity of the identity data they provide and verifying that data.
The dierences between the three cases mentioned above causes confusion as each involves dierent information and
has dierent legal ramications and requirements. While authentication is demonstrating ownership and control of a unique
feature connected to an identity over time, identity verication and validation check if the information represents real data
and aim to prove that the specied identity attributes are actually connected to a person, entity, or thing that they are
intended to represent.
Strong Customer Authentication
In this chapter, we will be focusing more on explaining authentication and addressing strong customer authentication.
This regulation will apply to online payments within the European Economic Area (EEA) where the cardholder’s bank
and the business’s payment provider are both in the EEA. However, some businesses outside of Europe may also be
impacted depending on how European issuers implement the new authentication rules.
The SCA requirement is applicable to all electronic payment transactions that do not benet from an exemption and is
based on an authentication using two or more elements. The elements are categorised as knowledge (something that
only the user knows, e.g., a password, answers to personal questions, PIN), possession (e.g., something that only the
user possesses, e.g. a debit card or mobile device), and inherence (something that user is, e.g., ngerprints). The elements
used must be independent from each other, and the two elements used for an authentication must belong to dierent
An Introduction to Online Authentication
and Stronger Authentication
Nevertheless, for certain transactions, the regulation also introduces exemptions to the SCA requirement. In brief,
the RTS exempts contactless payments at point of sale under EUR 50, low value (online) transactions under EUR 30,
transactions with trusted, predened beneciaries, subsequent recurring transactions, and low risk remote transactions
subject to certain conditions. According to Irena Dajkovic, a partner of DALIR law rm, other exemptions with more
limited application scope include those relating to transactions initiated by a legal entity (not consumer) through the
use of dedicated payment processes or protocols and subject to regulator’s approval, as well as those relating to access
to certain information (balance and/or payment transactions executed).
Transactions that do not meet these new authentication requirements or qualify for any exemption may be declined starting
September 14, 2019. However, according to some PSPs, 3D Secure 2, the new version of 3-D Secure rolling out in 2019,
has the potential to become the primary authentication method used to meet SCA requirements for card payments.
Why do we need strong authentication?
- To counterbalance the eects of multiple data breaches and protect customers against malicious actors – For instance,
in 2016, a third of US businesses have had customer information breached — including the information businesses
rely on to authenticate their customers. The mass compromise of passwords has led to an increased risk of fraud on
consumer accounts and networklevel attacks from credentialstung botnet attacks.
- To minimise false positives (benets for businesses: increase revenue by avoiding pushing good customers away)
As accuracy and customer loyalty are crucial for businesses, to win customer’s support, authentication solutions must
prove their eectiveness in both keeping bad actors out and ensuring a positive security perception for good ones.
- Because we have the technology - Mobile devices are a clear driver of traditional strong authentication. These devices
have increased the opportunity for businesses to leverage more than just passwords to authenticate their customers
and employees by facilitating both possessionbased authentication (e.g., device ngerprinting, SMSbased onetime
passwords (OTP), etc.) and inherencebased authentication (e.g., ngerprint scanning, voice recognition, etc.).
Strategies to bolster authentication
Cyber-criminals can be incredibly creative and determined when it comes to gaining access to consumer’s accounts
or enterprise’s data. To ght these actors, a number of tactics and strategies to bolster authentication have been
developed/presented by the private industry and public sector, including:
- Riskbased authentication – implementing authentication based on the degree of risk. Input data is analysed to deter-
mine which type of authentication is best to leverage following a determined degree of risk in a given transaction or
inter action.
- Continuous authentication – a variation of risk-based authentication. In this case, user’s actions through and across
sessions are taken into account when deciding the degree of access he/she has, or whether certain types of authenti-
cation are needed.
- Outofband authentication – uses a communication mechanism that is not directly associated with the device being
used to access the banking application or ecommerce site in order to facilitate a second mode of communication. Thus, it
can mitigate the risk that exists when the initiating channel is compromised or simply too insecure for the level of risk in
the transaction.
An Introduction to Online Authentication
and Stronger Authentication
According to Simility, a complex authentication process looks at various types of data, such as login, historical, cross-
channel, behaviour, device, geolocation, etc. to eectively and seamlessly decision the end user. Users are automatically
accepted, rejected, or required to step-up, such as in the case of high-risk transactions.
Also, the ability to tailor the authentication experience to the consumer’s comfort zone is important since this increases
the potential that the transaction will be completed, rather than abandoned.
Technology to the rescue
Financial services, banks, and merchants have dierent demands when it comes to users’ authentication, and some
factors and solutions are more vulnerable than others. Take for instance a password, PIN, and passcode which are vulne-
rable to interception or theft and replayed, or guessed versus facial recognition which is vulnerable to theft and emulation.
Facing the demands of the market and regulators, and at the same time seeking to repel attackers, those responsible
for choosing and implementing customer authentication face a herculean task. However, technologies such as
machine learning and AI and, of course, biometrics can help businesses ght the bad guys.
AI can evaluate a certain transaction, such as a log-in event, a shopping transaction, or a new-product application, by
using its unique contextual and transaction data, and come up with a negrain decision about its implied or inherent risk.
But, to be eective across geographies, analytics need a good consortium dataset and large pools of globally diverse risk
and fraud data to draw on.
But there’s a common misconception that this data invades privacy, which is not always the case. Vendors such as CA
Technologies anonymise all the data they use for predictive modelling to ensure that consumer privacy is protected. It is
the patterns of use over time that are important, and the proles that accumulate these patterns cannot be tied back to
an individual.
Another praised technology, successfully implemented by banks and other nancial services companies to keep their
customers safe, is biometrics. Their ability to perform without dependency on the user remembering or sharing a
password greatly enhances customer security while improving the user’s authentication experience. This technology
includes device fingerprinting, behaviometrics, fingerprint scanning, eye scanning, facial recognition, and voice
recognition; however, we will focus more on behaviour biometrics.
An Introduction to Online Authentication
and Stronger Authentication
Behavioural biometrics, sometimes known as passive biometrics, analyses how the user interacts with a device or
session. There are some 2,000 parameters that behavioural biometrics depends on and they give a clear indication of
someone’s unique identity. These range from monitoring human motion gestures and patterns to keystroke dynamics
and factors such as speed, ow, touch, sensitive pressure, and even signature formats. Behavioral pattern detection
technologies identify fraud by monitoring the user session to detect suspicious activities or patterns.
These anomalies manifest in a couple of ways:
- Transactional: The user is performing transactions that are out-of-pattern compared with normal behavior.
- Navigational: The manner in which the user is navigating the website is inconsistent with his or her usual pattern, is
inconsistent with the pattern of his or her peer group, or is indicative of the navigational pattern of a bot.
Even though biometrics represent a desirable alternative to passwords, a simple replacement of passwords with
standalone biometrics is generally not recommended. Such implementations would be comparably vulnerable to
compromises under realistic threat models. Integrated solutions such as multi-factor and multi-layer should be adopted
as acknowledged by 67% of industry professionals in a Mastercard and the Department of Computer Science at the
University of Oxford survey). Multi-factor approaches require users to respond to two or more explicit authentication
challenges (e.g., multi-modal biometrics). Multi-layer approaches combine a single explicit factor with other data element
that are typically invisible to users (e.g., device ngerprinting, geofencing, risk scoring).
Going further, when processing higher risk transactions, a number of biometrics can be combined in a stepup
process called multimodal biometrics. This happens in order to prove someone’s identity, known as Strong Customer
Authentication. Even more, if the customer uses their ngerprint, face, or PIN code to unlock their device, banks can
now pair that same user verication method with strong cryptographic protocols made available through ondevice
platform APIs, to allow customers to securely access their accounts online in full compliance with PSD2 strong customer
authentication requirements, on both apps and websites.
Still, no single method of authentication will always be suited for every situation. Companies are advised to adopt
approaches that use multifactor authentication, while also taking into account location, behaviour analytics, and numerous
other indicators of identity.
An Introduction to Online Authentication
and Stronger Authentication
Data breaches have become commonplace among global
headlines and newsfeeds, a painful fact of life until you become a
victim yourself, and realise the wholescale devastation breached
identity data can reap on your day-to-day life. The onus is squarely
on businesses to ensure they have the appropriate defences in
place to protect their customers, as well as safeguard their own
However, keeping personal data safe has become increasingly
challenging for businesses, who must contend with the evolving
demands of the digital economy amid ever more savvy, global
cyber criminals. Businesses are tasked with having to stay one
step ahead of the fraudster, no easy task when cybercriminals are
launching increasingly sophisticated and organised attacks, using
near-perfect identities created from piecing together breached
credentials so readily available on the Dark Web.
The intrinsic link between stolen identity data and attacks is
clearly evident through analysis of the ThreatMetrix Identity
Abuse Index. With the largest spikes in the index associated with
the biggest breaches reported in the news, the Index is a clear
indicator of how the exploitation of stolen identity information
is impacting the size and scale of global attacks. These volatile
attacks are deployed to give cybercriminals access to everything
they need in order to turn a prot with stolen creden tials. Whether
it be opening fraudulent new accounts, taking over existing
ones, applying for fraudulent loans, making illegal pay ments or
going on illicit shopping sprees, fraudsters are not only making
a monetary impact on the businesses they target, but also
threatening brand, reputation, and customer loyalty. Perhaps the
clearest indicator of the impact of breached identity data is the fact
that around one in ten new account creations in the ThreatMetrix
Network is fraudulent, and for some industries this gure can be
even higher.
Reimagining Identity in the Post-Data Breach Era
Alisdair Faulkner | Chief Identity Ocer | LexisNexis Risk Solutions
About Alisdair Faulkner : Alisdair Faulkner leads the commercial markets and strategy function for fraud
and identity management at LexisNexis Risk Solutions, Business Services. He was co-founder and
Chief Products Ocer for ThreatMetrix culminating in the 2018 acquisition by LexisNexis Risk Solutions.
He now oversees the combined fraud and identity solutions for LexisNexis Risk Solutions and the
ThreatMetrix Digital Identity Network.
Click here for the company profile
About ThreatMetrix: ThreatMetrix, A LexisNexis Risk
Solutions Company, empowers the global economy to
grow protably and securely without compromise. With
deep insight into hundreds of millions of anonymized
digital identities, ThreatMetrix ID delivers the intelligence
behind 110 million daily authentication and trust deci-
sions, to dierentiate legitimate customers from fraud-
sters in real time.
Thus, identity has become central when talking about success in
the digital economy. In a post-data breach era, businesses must
strive to re-establish trust online and gain insight into the true
identity of customers.
However, with consumers moving seamlessly between their o
line and online personas, across both their corporate and per-
sonal lives, businesses are faced with a myriad of challenges in
ascertaining the true identity of transacting users. Muddying the
waters further is the fact that individuals can behave dierently
and show different offline personas depending on the circum-
stances, for example, subscribing for media services online versus
applying for a business loan.
Traditional fraud and identity management is failing to keep pace
with this evolving fraud landscape – siloed and disjointed techno-
logies built to defend against various threat vectors introduce
unnecessary friction for the user, at excessive cost to the enter-
prise. Dierent ways of assessing users at dierent customer touch
points often means asking customers to jump through multiple
hoops to prove who they are – again adding friction to the overall
user experience.
Businesses can meet these competing priorities – protecting
against fraud while providing a frictionless user expe rience – by
having a complete 360-degree understanding of who they are
transacting with – anywhere, anytime, and via any channel.
But how can this be achieved? The secret to success is linking
the multi-faceted parts of an individual’s true identity in a way that
is actionable across multiple channels. The ability to join the dots
between a person’s oine and online identity requires access to
the most comprehensive sets of data and sophisticated techno-
logy to create and analyse linkages to form actionable intelligence
that can be used in real time.
1. Digital Assessment: To gain a truly 360-degree view of identity,
businesses should incorporate identity attributes seen during
digital touchpoints such as username and passwords, email
addresses, online account history and behaviours, social
networks, device identication, and geolocation.
2. Identity Verication: Involves linking attributes of an individual’s
digital identity to authoritative data sources from a person’s
oine records. This includes identity verication based on utility
bills, car registrations, and governmentissued identiers such
as social security numbers.
3. Analyse Fraud Risk: Advanced linking technologies and
machine learning can then correlate these disparate data
points and turn this into actionable intelligence on risk through
fraud scores and reason codes; determining velocities and
frequencies that are indicative of trusted versus suspicious
4. StepUp Authentication: For activity that shows elevated risk
analysis the nal step is deploying stepup authentication, for
example knowledgebased authentication, secure notications,
or biometrics. Strong customer authentication that integrates
seamlessly with risk-based authentication, based on identity
assessments, is key to delivering maximum security with
minimal customer intervention.
The combined understanding of physical and digital identity
interactions allows businesses to respond quickly and more
comprehensively to the vast number of threats facing the global
economy. Solving the problem of identity in the digital age will
enable a seamless and comprehensive approach to fraud and
identity risk management to help companies drive online revenues
by making faster decisions, reducing online fraud and combating
emerging threats.
The age of digital transformation has arrived, revolutionising the
nancial services industry with new ways of doing business any
time, anywhere. With a growing array of digital banking channels
available, customers seemingly have infinite possibilities for
conducting nancial business. At the same time, this expansion of
banking channels increases the risk of fraud.
Winning in the digital era means rising to the challenge of mee-
ting an entirely new set of customer expectations. As Hari
Gopalkrishnan, CIO of Client Facing Platforms at Bank of America
put it, ‘Our customers don’t benchmark us against banks. They
benchmark us against Uber and Amazon.’ To succeed, FIs must
manage digital risk so that it doesn’t stand in the way of digital
opportunity. In the middle of the fulcrum sits customer experience.
Top ve areas for digital opportunity
There are ve key areas of digital opportunity for the nancial ser
vices industry, as follows:
Fintech is transforming the industry. Digital wallets, cryptocurrency,
blockchain, and other Fintech oerings are redening banking and
nancial services in a multitude of ways, putting traditional FIs at
risk of losing business to them. Increasingly, traditional banks are
rapidly innovating to provide more of the kinds of digital services
their Fintech competitors oer.
API economy and Open Banking
The API economy offers customers the option of convenience
such as being able to link their accounts with other services
(utility payments, for example) without the FI having to build out a
complex technology infrastructure to support the new capability.
In some cases, this may be more than an opportunity; it may be
an obligation. For example, the European Union’s (EU’s) Payment
Services Directive II (PSD2) requires banks doing business in the
EU to open access to their systems to payment services and data
3D Secure 2.0
Card issuers and issuing processors have started or are planning
to embark on the journey of adopting EMV 3-D Secure (AKA 3-D
Secure 2.0). The opportunity for 3-D Secure 2.0 lies in its adoption
of consumer-friendly features such as the elimination of enrolment
pop-ups, full integration into the shopping experience, and faster
authentication. By reducing the annoyance factor, these changes
have the potential to lead to more approved transactions and
more revenue.
Mobile banking
Mobile banking has become a staple of consumer offerings.
In fact, the mobile channel has become the predominant and
preferred channel for consumers.
Adaptive Authentication: Balance Opportunity and Risk in an Omnichannel World
Mathew Long | Senior Advisor, Fraud & Risk Intelligence | RSA
About Mathew Long: Mathew Long is a Sr. Advisor for the RSA Fraud and Risk Intelligence division.
Mathew leads the global go-to-market eorts for RSAs consumer authentication and fraud intelligence
solutions. Mathew is a prolic blogger and a regular presenter at industry events and media engagements.
For the past six years, he has focused on working with leading nancial institutions on anti-fraud and
cybercrime prevention strategies to reduce fraud and improve customer experience.
About RSA: RSA, a Dell Technologies business,
oers business-driven security solutions that uniquely
link business context with security incidents to help
organisations manage digital risk and protect what
matters most. RSAs award-winning cybersecurity
solutions are designed to effectively detect and
respond to advanced attacks; manage user identities
and access; and reduce business risk, fraud, and
cybercrime. RSA protects millions of users around the
world and helps more than 90% of the Fortune 500
companies thrive in an uncertain, high-risk world.
According to RSAs Quarterly Fraud Report, in the last three years,
transactions from mobile apps have increased over 200%, and
the overall volume of activity in the channel now outpaces that
of the web with 55% of all transactions conducted from a mobile
app or mobile browser. As a result, FIs are expanding their mobile
channel to provide new services to their customers while meeting
their demands for secure, convenient account access.
Internet of Things (IoT)
While banking does not lead the list of today’s top IoT applica-
tions, the prospects for IoT-based financial transactions look
good nevertheless – particularly in the payments segment. IoT
is an emerging area, deemed the next evolution in banking and
shopping convenience. The concept of ‘human-not-present’
transactions where IoT devices interact directly with payment
systems is not far o and it will enable more personalised services,
facilitate usage-based fees, and much more.
Stop fraud, not customers
As the array of digital channels grows, so does the need for
security technology that can detect and prevent fraud in ways that
are frictionless for customers. Adaptive authentication solutions
leverage machine learning models to assess fraud risk based on
contextual information such as device identication, IP address,
user behaviour, and fraud intelligence (eg mule accounts). Its
nonintrusive nature, flexibility, and ability to manage fraud risk
across multiple channels makes adaptive authentication an ideal
solution for FIs looking to deploy strong security to large customer
Adaptive authentication technology can achieve fraud detection
rates of 95% with minimal customer intervention and it allows for
integration with numerous step-up authentication methods in the
event of a high-risk scenario, including out of band SMS or email,
biometrics, transaction signing, and more.
With so many channels for customers to interact, omnichannel
fraud detection has become a hallmark of adaptive authentication.
Back when ‘multiple channels’ at most meant a branch bank and
an ATM network, this wasn’t so much an issue.
But today’s banking channels are also likely to include online
banking, chat support, mobile banking, call centre, IVR, and third-
party services, with more channels, such as IoT devices, on the
way. In this environment, siloed operations are both ineective
and unsustainable.
Adaptive authentication allows operations to be carried out as a
whole rather than an array of discrete parts. This eliminates the
need to build and maintain a separate infrastructure (including
separate point solutions for fraud detection and prevention) for
every channel. Instead, all channels both online and oine
can share knowledge and awareness of a customer’s interactions
and lead to streamlined operations, a more secure banking
environment, and a smoother customer experience.
Rules have now come into eect, requiring banks
to share their customers’ nancial information with
other authorised providers using open Application
Programming Interfaces (APIs). However, this
makes banks dependent on the security of the Third
Party Providers (TPPs) using these APIs. What are
the possible risks of this new Open Banking era?
Under the Open Banking initiative, institutions must open their
APIs to give TPPs access to their customer data. In other
words, if a bank’s customers want to use one of these TPPs,
the bank must give the TPP access to its stored data about
them and allow the TPP to serve these customers via the open
communication interface.
Open Banking benets nancial institutions by enabling them to
build new business models around a variety of innovative and
more personalised customer services. But it also exposes a
bank’s customers to a greater risk of fraud since their nancial
data must now be shared with multiple TPPs. The problem is not
so much that the data is being shared through Open APIs, but
that it might be shared without properly authenticating both the
TPP and user.
In this context, I would like to emphasise two points that will play
a critical role in the future. First, banks must prevent data loss,
identity theft and non-compliance with data protection regu-
lations by using identity verication and fraud prevention solut
ions that ensure personal data is shared only with the consent of
its genuine owner. Second, banks will need to ensure that each
TPP is known, trusted, and has strong enough security policies
in place to safeguard all shared data.
Strong customer authentication is especially important and must
be the central element in the Open Banking API ecosystem. It must
be a priority both for banks, which already understand that sen-
sitive data requires high security and protection, as well as for
TPPs, which are only at the beginning of their learning curve.
What security measures should banks adopt to
address these threats and challenges?
Banks have come to realise that they will be the central point
of authentication in this growing nancial ecosystem. When data
must be shared with a TPP, the bank is in the best position to
deliver a seamless authentication experience that does not com-
promise security. Customers will not tolerate an authenti cation
experience that meets security requirements at the expense
of convenience. They have come to expect easy, on-the-go
online access and mobile transactions and will not accept time-
consuming processes in this emerging Open Banking ecosystem.
Olivier Thirion de Briel | Global Solution Marketing Director | HID Global
About Olivier Thirion de Briel: Olivier Thirion de Briel is Global Solution Marketing Director for the banking
sector at HID Global. In this role, Olivier leads the banking strategy and product marketing for the IAM
solutions business unit. Prior to joining HID Global, Olivier led the cloud strong authentication oering at
OneSpan (former Vasco) and the Oberthur Technology’s strong authentication product line. Olivier holds
an MBA from INSEAD, as well as an MSc in computer and electronic science.
HID Global
The Paypers interviewed Olivier Thirion de Briel, Global Solution Marketing Director at HID Global, about what role authenti
cation plays within the Open Banking ecosystem. Following are takeaways from our discussion.
As nancial fraud incidents
grow in digital banking channels
it is imperative that institutions
protect their customers.
Click here for the company profile
As these technologies are brought to the Open Banking API
ecosystem, we will also see financial transactions based on
connected devices. Within this ecosystem, the use of static multi-
factor authentication methods will decrease and we will see
a migration to continuous data analysis that improves risk-miti-
gation decision-making and creates a more secure transaction
Different authentication models have their own
charac teristics and security implications. Can you
please describe the ideal authentication process?
In this new digital era, the authentication process must be
based on an adaptive security approach in which the level of
complexity depends on the risk associated with the transaction.
This risk level is established based on multiple parameters
including malware detection, geolocation, IP address, and how
the customer is using a mouse or keyboard or displaying other
behaviours. Some solutions can evaluate a transaction’s risk
level based on characteristics of the user device and its browser
and other attributes.
If the risk level based on these parameters is dened as low,
authentication may only require a username and password. If it is
dened as high because the transaction is being conducted with
an unknown beneciary at an unusual place and time, additional
authentication methods may be required to prove the user is
who he or she claims to be.
It is also important to understand that growing use of connected
devices has expanded the attack surface for financial fraud-
sters. Risk-based advanced authentication will need to take into
account the entire environment in which customers are trans-
acting to provide the necessary protection.
Since PSD2 allows third party providers to access
customers’ payment account data, in what way
is this directive aligned with GDPR? How will
discussions about data analytics evolve over the
next 5 years?
Open banking is about sharing data and making it available to
TPPs. GDPR, on the other hand, aims to ensure that nobody can
steal personal data. In fact, the goals of GDPR, Open Banking
and PSD2 are all aligned around giving data ownership back to
users. This is where security plays a key role, and GDPR brings
an additional layer of requirements for securing sensitive data.
Machine learning and AI will enable banks to collect and analyse
data so they can make smarter real-time decisions about the
next action to take when a threat is detected, including whether
to approve, block or reject a transaction. Adaptive authentication
processes will enable them to dene security levels based on
existing risk.
About HID Global: HID Global is the leading provider
of trusted identity and access solutions for people,
places and things. We enable organizations and
enterprises in a variety of industries such as banking,
healthcare, and government to protect digital identities
in a connected world and assess cyber-risk in real-
time to deliver trusted transactions while empowering
smart decision-making. Our extensive portfolio oers
secure, convenient access to on-line services and
applications and helps organizations to meet growing
regulatory requirements while going beyond just simple
Online authentication is an intelligent tool that allows companies
to differentiate legitimate activity from fraudulent behaviour to
make sure only the right users get through. However, as intelligent
as it may be, there does still remain a challenge in making sure the
wrong users with the right credentials don’t cheat their way past
this barrier. This means that no company can ever really be 100%
sure about the true identity behind an online user.
Approximately 98% of human transactions are legitimate, meaning
only 2% are fraudulent. With such favourable odds, one would
think it was a given that businesses shouldn’t be quick to treat
all customers as potential fraudsters. But some do. And in doing
so, instead of protecting their business, they end up pushing loyal
customers away. We could conclude that overly strict defence
mechanisms won’t let all legitimate customers through. On the
other hand, interruptive authentication methods cause transaction
abandonment and loss of customers.
Fraudsters continue to nd ways to overcome traditional authen
tication methods, as we have grown accustomed from them to
do so. Static defence mechanisms do not prevent all cases of
fraud: login data is being bought on the dark web, CAPTCHA
is being outsmarted by bots, true geolocation is being hidden
via proxy servers, device fingerprinting is being imitated by
emulators, and multi-factor authentication is being surpassed
when session takeover occurs. That’s why the industry has been
forced to think beyond passwords and secret questions, and
research advanced authentication methods.
As unique as a ngerprint
The way we subconsciously behave on our phones or computers
– how we hold, scroll, swipe, click, tap, or type – is as unique as
our ngerprints.
By using sensors in touchscreens or codes on websites, data can
be collected invisibly to the user. Multiple interactive ges tures can
be constantly analysed — including how the person is holding
the device or the speed and rhythm in which they’re using their
mouse. Endless amounts of these data points to gether form a
digital ngerprint and can be used to establish a user’s identity.
With the aid of these behavioural biometrics, companies will not
only be able to accurately dierentiate between legitimate custo
mers, fraudsters and non-human behaviour (eg BOTS, malware, or
Random Access Trojans), but they will also save costs with fewer
suspicious cases to check manually.
Arvato Financial Solutions
Seamless and Secure Online Authentication: A Solvable Goal?
Robert Holm | Senior Vice President Fraud Management | Arvato Financial Solutions
About Robert Holm: Robert Holm is Senior Vice President Fraud Management at Arvato Financial
Solutions. With an experience of almost 20 years in setting up and growing new businesses, he leads
the strategic development and internationalisation of the fraud management division.
Until passive behavioural
biometrics, online fraudsters
had a method for overcoming
the security of traditional
authentication methods.
About Arvato Financial Solutions: Arvato Financial
Solutions provides professional financial services
centred on cash ow in all segments of the customer
lifecycle: from identity, fraud, and credit risk
management, to payment and nancing services and
debt collection. Our team made up of proven and
reliable experts in around 20 countries gives businesses
the best possible platform for growth.
And it can do more than reducing fraud threats and financial
losses. Companies are also able to minimise false positives and
increase revenue by avoiding pushing good customers away.
Additionally, leveraging the user’s behavioural biometric data
means businesses receive additional valuable insights about their
customers. This allows for further optimisation of the customer
journey and user experience – improving customer loyalty and
encouraging higher conversion. In fact, Gartner states that by
2022 digital businesses with a great customer experience during
identity corroboration will earn 20% more revenue.
The great advantage of this new authentication method is that
even if fraudsters try to use stolen passwords and other personal
information, behavioural biometric monitored accounts can still
be secured, as this type of information can’t be stolen, faked, or
Behavioural biometrics dierentiators
In contrast to other protection methods, such as active physical
biometrics, there are many positives when it comes to passive
behavioural biometrics:
It does not depend on special scanning hardware and is inde
pen dent from devices or locations.
Authentication is not onetime validation, but a continuous pro
cess from check-in to check-out – protecting transactions inclu-
ding registrations, purchases, payments, and money transfers.
No extra user actions are required. It is frictionless and seamless
and not aggressive or irritating, like most security barriers.
No personal data is collected or stored, complying with the
European Union’s General Data Protection Regulation.
Securing companies, protecting customers
The behavioural biometric data is compared to the historical
behaviour of the user and average behaviour patterns. Based
on analysed signals of each user prole, the system generates
a ‘trust score’ with proprietary machine-learning algorithms.
Assuming that the average person’s phone habits will change,
say, on a Saturday night compared to a Wednesday morning,
the behavioural biometrics software then calculates whether
someone is really who they are claiming to be.
As diverse protection methods are needed to cover a wide
range of fraud cases, Arvato Financial Solutions oers a broad
solution portfolio for different types of threats. Based on our
longstanding industry and marketspecic experience, the fraud
and nancial experts working in our teams oer a customised
approach to each of our clients to provide the optimal solution
for their particular needs.
Based on each company’s individual goals, the industry land-
scape, the fraud prevention methods in place, and the fraud
mana ge ment architecture, we determine which specic solution
or module combination is the best match for each business.
Arvato Financial Solutions is the backbone for growth, providing
a holistic approach to help companies optimise their processes
and customer experience, and protect their revenue and repu-
tation while providing protection against fraud tailored to specic
Click here for the company profile
True customer satisfaction means optimizing expe-
riences and relationships from start to nish
In the digital age, businesses face the constant challenge of deter-
mining legitimate customers from fraudsters. Fraudsters target
a variety of points along the transaction process, but some of
the most common are new account creation, transactions, and
account recovery. Enterprises must walk a ne line to ensure that
appropriate measures are taken to prevent fraud while also provi-
ding a low-friction user experience. While the sophistication and
frequency with which fraudsters attack has increased drama-
tically, so have the tools businesses can use to combat them.
One of the most prevalent forms of fraud is synthetic identity fraud,
which results in direct losses of around USD 118 billion each
year. This is a hard cost for many industries such as insurance,
healthcare, and banking who typically rely upon flawed legacy
authentication methods such as increasingly complex passwords,
OTPs via text and email, and knowledge-based authentication
However, as enterprises increase the complexity of the authenti-
cation process, legitimate users are confounded by that com-
plexity leading to false positives and by users circumventing the
intent of the systems (eg reusing passwords).
These legacy methods have been further compromised by the
numerous highprole breaches of retailers, healthcare providers,
government records, credit bureaus, and hospitality chains,
resulting in over 10 billion data records reported as being exposed
since 2013 (Gartner Market Guide for Online Fraud Detection
Published 31 January 2018 - ID G00318445), and those are just
the ones that we know about!
With so much personal information readily available, fraudsters
have become procient at using the same data to commit multiple
fraud attempts. Through the use of bots, fraudsters can submit
tens of thousands of applications in a single day, typically from a
remote country, and only need a handful to pass through in order
to prot.
While the direct cost of USD 118 billion seems a staggering
number, it is not the total cost. I had the opportunity to work
directly with the fraud and risk team of a large US S&P 500 Bank
who illustrated the extent of unseen opportunity costs. Thousands
of potential customer applications were being rejected due
to authentication concerns. While these applicants may have
been fraudulent, they may also have been qualied customers.
Moreover, the opportunity cost losses were not limited to new
Account Takeover and Step Up Authentication
Andrew Gowasack | Cofounder and Managing Director | TrustStamp
About Andrew Gowasack: Andrew is Cofounder and Managing Director of Trust Stamp. As a co-leader
in Emergent’s global identity initiatives, Andrew is engaged with the delivery of identity-related services
across all of Emergent’s verticals, but his primary focus is building strategic partnerships around the
About TrustStamp: A multi-factor biometric platform
with inbuilt de-duplication that can be augmented
with social media and other data mining and identity
warranties. Among the platform’s unique factor is a
shareable non-PII hash that tokenizes identity and
can embed both encrypted data and pivot points to
external data.
A growing number of existing customers were locking themselves
out of their accounts because they could not answer their KBA
questions or they could not receive the OTP as they had changed
their cell phone number. The standard protocol for the bank was
to close these accounts.
These challenges are rampant on digital platforms. On average,
for each account that is erroneously closed and each genuine
applicant declined, there is an opportunity cost of USD 61 per
incident. To make matters worse, there is an additional unquan-
tified loss of goodwill. Just like the direct cost of fraud, these
oppor tunity costs impact the companies’ bottom line.
Because of their potential for security, as well as usability, a growing
number of enterprises are implementing biometrics ranging from
ngerprints to voice, to facial recognition. In addition to better
technology for collecting biometrics (eg improving smartphone
cameras), customers are becoming increasingly accustomed to
using them. While biometrics’ usability may resolve many authen-
tication barriers, not all of them provide the technology needed to
reduce the direct and opportunity costs of fraud.
Biometric solutions that can resist replay attacks and prove
liveness partially resolve the issue of bot-initiated interactions. If a
live biometric is required for applications, transaction approval, or
account recovery, and that biometric is compared not just to the
instant transaction but all prior biometrics from all transactions,
then a fraudster needs a dierent live human for every transaction.
For many biometric solutions, a biometric sample is compared
to a source of assumed truth such as a national ID document or
passport, and if there is an apparent match, identity is esta blished.
The problem is that fraudsters create sophisticated fake IDs, some-
times using the same machines as legitimate issuing authorities,
or they obtain “real” IDs for stolen identities. While this is not as
scalable as blanked bot applications, it allows for repeated fraud
attempts and has a far higher probability of success.
By using only biometric solutions that test liveness, while securely
and compliantly storing biometric data, enterprises can compare
the current biometric sample to all previous biometrics and spot
instances where two or more users share the same biometrics.
This deduplication process eliminates the possibility of the same
person making multiple applications under dierent identities.
Click here for the company profile
Why has authentication become such a hot topic?
First, let’s compare Europe and North America because the
landscape and the drivers are a bit dierent. In Europe, PSD2 is
making it a legal requirement to apply authentication to any type
of remote electronic interaction that carries a risk of fraud. In North
America, the focus is more on optimising the customer experience
by moving toward the frictionless checkout.
The card associations – Visa, Mastercard, and American Express –
are also introducing global rules to make the use of these authenti-
cation programmes mandatory. Thus, ecommerce purchase
authen tication is critical in both geographies.
With the PSD2 regulation and new rules from the card associa-
tions, authentication has become the largest, brightest target on
the ecommerce radar. And it’s happening just as the 3-D Secure
authentication protocol is launching. So the timing of EMV 3DS is
Because we co-invented the 3-D Secure protocol, and we’re one of
the few providers that have been running the platform for 20 years,
we can help get you there in the most ecient way. And I should
add that we were the rst to authenticate a EMV 3DS transaction.
How is articial intelligence changing the authenti
cation experience?
AI can evaluate any given transaction, using its unique contextual
and transaction data. Whether it’s a log-in event, a shopping
transaction, or a new-product application, analytics can make
a fine-grain decision about its implied or inherent risk. This is
important for both driving out fraud and providing frictionless
For example, we’ve got hundreds of millions of identied devices
associated with billions of ecommerce payments globally. We know
if those past payments were high risk, conrmed as fraudulent, or
conrmed as good. So we can say “We recognise this one; we’ve
seen it before,” and associate the device with known good or
known bad behaviour.
James Rendell | Vice President, Payment Security Strategy | CA Technologies
About James Rendell: James Rendell heads Payment Security Strategy and Product Management for
CA Technologies. James is a recognised fraud and security expert, covering topics such as mobility,
cryptography, ecommerce, and network and infrastructure security.
CA Technologies
Ecommerce continues to grow at an astounding rate and so does online fraud. According to Javelin Research, cardnot
present (CNP) fraud accounts for 81% of total fraud, representing billions of dollars in losses annually. To address this crisis,
the industry is taking a fresh look at transaction authentication.
With the PSD2 regulation
and new rules from the card
associations, authentication has
become the largest, brightest
target on the ecommerce radar.
Reconciling Consent in PSD2 and GDPR
Click here for the company profile
You need this kind of expertise in knowing how to apply the techni-
ques of data science. It’s easy to make mistakes and misapply
them, and there are plenty of war stories where a model was being
biased the wrong way.
In the end, the more data you have, the more powerful the oerings
you can build based on predictive analytics. It’s about how you
leverage data to build the advanced machine learning needed to
optimise user experience and drive out fraud – while protecting
consumer privacy at the same time.
This intelligence, grounded in the ecommerce space, is a uniquely
powerful consortium dataset to have. In the end, virtually every online
crime, whether an account takeover, identity theft, or a mal ware
compromise, ends up in a fraudulent payment attempt somewhere
– often through the use of stolen user credentials such as online
banking or card details.
On top of this, competing across multiple digital channels is very
important to our customers. By providing a central, omnichannel
platform for authentication of card and non-card ecommerce pay-
ments, we make it possible to manage these risks and customer
experience demands.
What kinds of data do you need for risk analytics?
To be useful across geographies, analytics needs a really good
consortium dataset. You need the largest possible pool of globally
diverse risk and fraud data to draw on. But there’s a common
miscon ception that this data invades privacy, which is not the
case. All the data we use for predictive modelling is anonymised
to ensure that consumer privacy is protected. It is the patterns of
use over time that are important, and the proles that accumulate
these patterns cannot be tied back to an individual.
Predictive analytics is actually a well-established fraud prevention
discipline. It extended into the ecommerce 3-D Secure scene a
decade ago, which is when the focus on gathering data to support
its development became our core business. We have the longest
established dataset in the ecommerce payment fraud eld and we
believe we have the largest market share of issuers in this space.
We service more than 13,000 card portfolios and well over a billion
transactions a year. Having a globally diverse, large consortium of
data for the analytics to chew on, as it were, is really important.
Otherwise, you end up with predictive analytics that are trained
out of very limited datasets, useful only for point problems.
How do you build an AI engine to ght fraud?
Certainly, the most important factor is that we employ a group of
world-class data scientists with, when you add it all up, hundreds
of years of experience in payment fraud.
About CA Technologies: CA Technologies, a
Broadcom company, is an industry leader in payment
and identity fraud prevention, with friction-free
transaction authentication powered by patented
artificial intelligence. As a pioneer in analytics for
online fraud, CA delivers a unique 360º view of
transactions for issuers, processors, and merchants,
across all payment schemes. Learn more at ca.com/
The payments and commerce landscape has undergone signi-
cant changes in recent years. At a local level, commerce and
banking moved to a digital-first, standard format. At a global
level, and specically in developing markets, there has been a
huge transition from “mum and dad” shops straight to online
commerce. People no longer need banks or shops; they need
banking and commerce services.
However, as much as this oers new and exciting online opportu
nities to businesses, unscrupulous individuals are also taking
advantage of easy-to-access fraud tools and freshly breached
data, exploiting vulnerabilities and targeting weaknesses in the
security infrastructure of unsuspecting organisations.
Managing risk in a “post-breach world”
Companies are now operating in an environment in which they
have to assume, even with the most sophisticated security solu-
tions, that there are no cast-iron guarantees in a “post-breach
normal” world. Managing risk in this environment needs to be
handled in real time.
The most pressing challenge for companies is to balance cus-
tomer experience eectively with security and regulatory issues.
Customers have become accustomed to frictionless digital
expe riences and want payments to be made immediately. At the
same time, cybercriminals are constantly evolving their attacks
and using increasingly sophisticated techniques.
An increasingly complex regulatory environment that necessi-
tates businesses to comply with PSD2 (Second Payment Services
Directive), faster payments and open banking, adds a further
burden to companies.
Fraud management is no longer a linear decision, with multiple
factors needing to be considered and weighed in real time,
which is something traditional tools are unable to accomplish.
As cyberattacks become ever more complex, sophisticated,
and cross-channel, companies need a solution that can change
as business needs change, yet that can also protect against the
evolving fraud landscape.
Balancing multiple priorities
Strong, but frictionless authentication is the key to oering an
elegant customer experience and minimising fraud, while also
stay ing in compliance.
Complex Fraud Threats Call for Adaptive Detection Tools
Rahul Pangam | Co-Founder and CEO | Simility
About Rahul Pangam : Rahul Pangam is the Co-Founder and CEO of Simility. He’s an industry veteran,
with impressive experience from Google, who is dedicated to empowering fraud ghters with the most
adaptable, scalable, and accurate fraud analytics platform.
Click here for the company profile
About Simility: Simility oers real-time risk and fraud
decisioning solutions to protect global businesses.
Simility’s offerings are underpinned by the Adaptive
Decisioning Platform built with a data-first approach
to deliver continuous risk assurance. By combining
artificial intelligence and big-data analytics, Simility
helps businesses orchestrate complex decisions to
reduce friction, improve trust, and solve complex fraud
Although companies have attempted to improve security
through dierent authentication methods, such as knowledge
based authentication (KBA) and multi-factor authentication
(MFA), these methods are not without shortcomings. KBA lacks
security because it is dependent upon “shared secrets” between
users and servers, and MFA causes friction, which frustrates
the customer. Businesses need a solution that empowers them
to seamlessly balance multiple competing priorities without
increasing friction, operational costs, or false positives.
Using data as a strategic advantage
As fraud continues to grow and cybercriminals become even more
adept at circumventing security tools, it’s imperative to maintain
a seamless experience for legitimate users. Using various types
of data sources and applying concepts of machine learning for
greater visualisation and accurate insights to drive effective
fraud management is critical. Companies that can turn data into
a strategic advantage will establish an edge over their compe-
Built with a data-first approach in mind, Simility’s Adaptive
Decisioning Platform oers a holistic view of the end customer.
This helps companies orchestrate complex and accurate
decisions to reduce friction, detect fraud patterns, and assist
with regulatory requirements.
Simility’s complex authentication looks at various types of data,
such as login, history, cross-channel interaction, behaviour,
device, geolocation, etc to eectively and seamlessly decision
the end user. Users are automatically accepted, rejected, or
required to step-up, such as in the case of high-risk transactions.
With Simility, companies do not only have the processing power
to analyse huge datasets, but they can also customise user
interactions. By personalising services based on risk factors,
such as location, device and behaviour, trusted users can be
identied and treated as such and provided with a more seamless
experience, leading to increased customer satisfaction.
From digital banking to online commerce, the consumption of
on line business services has changed consumer behaviour and
expectations. Gone are the days when people were willing to
stand in line to open a bank account or checkout at a retail store.
Nowadays, they expect millisecond response at online market-
places. They want to use emerging payment types like digital
wallets. Peer-to-peer payments are on the rise. As a result, in
today’s digital economy, a well-orchestrated customer experience
in digital channels is a competitive necessity, not a luxury.
The reality of creating an optimal customer experience, however,
can be challenging. The cost of fraud for the nancial services
market has never been higher, owing largely to the proliferation
of fraudulent online accounts. Competing objectives of revenue
growth and risk mitigation mean that while businesses in this
market are working to ensure that they can detect fraudulent
accounts before they can wreak havoc, the added layers of
authentication add friction to the customer user experience.
The Q2 2018 Fraud Index Report from my own company,
DataVisor, showed a startling trend: as many as one in ve cloud
user accounts may be fake. In fact, for some cloud services, more
than 75% of accounts may be used by hackers. More than 40%
of application fraud comes from coordinated attacks, with single
fraudsters operating multiple fraudulent accounts.
To combat this ever-growing rise in fraud, organisations are using
mul ti ple layers of authentication factors to verify the validity of a
user’s identity.
The emergence of n-factor authentication
Several types of authentication factors can typically come into play
in preventing fraud, which are often combined for comprehensive
protection. They include password factors (from ATM PINs to
computer passwords), SMS factors (two-factor authentication
codes), knowledge factors (username and passwords), possession
factors (smart cards), and biometric factors (ngerprints or voice
prints – or even optical scanning).
Proving online identity used to mean combining two or more of
these factors, commonly referred to as “multi-factor authentica-
tion.” This approach has been proven eective in enterprises of
all sizes. In July 2018, Google reported that phishing attacks of its
employees almost stopped after the company began requiring the
use of two-factor authentication security keys across its business.
While multi-factor authentication increases the chances of detec-
ting a fraudulent account or even possible identity theft, it is extre mely
cumbersome for users. In some cases, authentication happens
to be based on data purchased from third parties, which consu-
mers consider to be private information – like mortgage payments.
Users typically balk at sharing so much personal information, and
see it as an invasion of their privacy.
Moreover, multi-factor authentication does not even provide as
much robust security as one might assume. Take, for exam ple, the
recent Facebook attack, where more than 30 million user accounts
were hacked.
The Journey Towards Zero Factor Authentication
Yinglian Xie | CEO and co-founder | DataVisor
About Yinglian Xie: Yinglian is the CEO and Co-founder of DataVisor, a successful AI-based fraud
detection technology company. Before founding DataVisor, Yinglian worked at Microsoft Research for
more than seven years on numerous projects focused on advancing the security of online services with
big data analytics and machine learning. Yinglian completed both her PhD and post-doctoral work in
Computer Science at Carnegie Mellon University and holds over 20 patents.
About DataVisor: DataVisor is the next-gen fraud
detec tion platform based on cutting-edge AI techno-
logy. Using proprietary unsupervised machine learning
algorithms, DataVisor helps restore trust in digital
commerce by protecting businesses against nancial
and reputational damage caused by fake user accounts,
account takeovers, and fraudulent transactions.
Attackers manipulated access tokens to compromise normal user
credentials. This is not surprising, especially when tokens are used
to represent authenticated users and there is no re-authentication
for subsequent interactions. The systems assume that these
tokens are from real users.
The identity of the future
While technologists are busy inventing new methods to add
another layer of authentication to identify users, at DataVisor, we
are exploring the utopian vision of “zero factor authentication”.
This vision uses advanced technologies to build a digital DNA
that integrates online behaviours (across device, activities, and
biometrics) to uniquely identify each customer. With artificial
intelligence, the reality of “zero factor authentication” is closer
than we think.
There are three critical elements to realising the vision of zero factor
(1) Robust data collection: a more negrained data collection
that forms the basis for deriving the digital DNA is imperative.
Today, organisations suffer from data loss as it trickles into
down stream systems. They lose their integrity and in that
pro cess lose valuable signals that could be used to build the
digital identity. To be eective, organisations have to look into
building and maintaining identities in real-time, using data
streams at their source versus in batch.
(2) Constant analysis of data: this is an analysis in which users
are continuously “re-authenticated,” in passive mode, instead
of using authentication at a given point in time.
(3) Transparency: when augmented with transparency and control,
users become part of the customer journey, have better control
and inuence over how their identity is being built and used, and
choose if they want to opt-in or opt out of zero factor authen-
tication. Many companies like Google are allowing users to
control the data they want to share and how that information
gets used, thus users can choose their “own journey.” The goals
are to gradually establish confidence and trust in this new
authentication paradigm, and to demonstrate that it is equally
secure, or can, in fact, be more secure.
The next generation platform needs to rethink digital identity and
authentication in a transformative way. Advances in technology
must be able to combine machine and human intelligence to
deliver zero factor authentication and not n-factor authentication.
Current authentication methods expose too many loopholes
– third-party apps, tokens, and APIs that can be leveraged by
Adding more layers of authentication simply means that as an
industry we have failed to build a path to building a better digital
identity. As AI becomes the driver for intellectual horsepower
within the organisation, authentication means better security,
greater trust, and personalised user journeys – all enabled by
Click here for the company profile
Time is money when it comes to ghting fraud. Organised crime rings,
fuelled with billions of compromised data records, are systemati-
cally and methodically targeting the nancial services value chain
with sophisticated card fraud, application fraud, and account
takeover attacks. The volume of the attacks continues to increase,
since there is little in the way of adverse consequences for the
criminals (i.e., jail time).
Another key challenge for nancial institution (FI) fraud executives
is that even as the threat environment continues to escalate and
rapidly evolve, FIs are under intense competitive pressure to make
the banking experience easier and frictionless (while regulators in
Europe appear to be taking the industry in a dierent direction,
thanks to the second Payment Services Directive’s requirement for
Strong Customer Authentication). In the face of these seemingly
contradictory mandates, many leading FIs are turning to orchestra-
ted authentication.
What is orchestrated authentication?
Nowadays, authentication is typically a onesizetsall activity, with
stepped-up authenticators applied universally, regardless of the
context of the transaction. For example, any time a retail-banking
customer tries to send a person-to-person payment or a commercial
customer tries to send a wire over a certain dollar amount, the user
must input a one-time password. Orchestration of authentication
seeks to better analyse the customer’s usual behaviour patterns
as well as the context of the transaction.
With orchestration, the friction of stepped-up authentication
is only applied when necessary, that is when the analytics ag
that the context of the transaction is unusual behaviour for the
The concept of orchestration can also consider the end user’s
preferences in authenticators since this is by no means universal.
The ability to tailor the authentication experience to the consumer’s
comfort zone is important since this increases the potential that
the transaction will be completed, rather than abandoned. An Aite
Group survey of consumers in the UK, US, and Singapore shows
differing preferences for authentication mechanism by age, by
country, and even by the frequency with which the consumer
engages in digital commerce. A few examples of these dierences
can be seen in the gure below:
Only 41% of consumers between 25 and 40 prefer username/
password, compared with 57% of consumers 65 and older.
56% of consumers between ages 18 and 24 prefer the ngerprint
biometric, compared with just 39% of consumers 65 and older.
This is understandable since ngerprints wear over time and the
ngerprint biometric is often dicult to use for seniors.
Younger consumers are more open to facial recognition techno
logies than older generations.
Aite Group
2019: The Push for Orchestrated Authentication
Julie Conroy | Research Director | Aite Group
About Julie Conroy: Julie Conroy is research director at Aite Group focused on nancial crime issues.
She has extensive product management experience working with financial institutions, payments
processors, and risk management companies, including several years leading the product team at Early
Warning Services.
About Aite Group: Aite Group is a global research and
advisory firm delivering comprehensive, actionable
advice on business, technology, and regulatory issues
and their impact on nancial services. With expertise in
banking, insurance, wealth management, and capital
markets, we partner with our clients, delivering insights
to make their businesses smarter and stronger.
Figure 1: Consumers’ Preferred Authentication
Method by Age
Source: Aite Group survey of 1,400 consumers in the UK, the
US, and Singapore, July 2018
How is orchestration achieved?
While intuitive in concept, orchestration requires advanced
analytical capabilities. To achieve the potential of orchestration,
FIs need to be able to harness the breadth of their customer data
and apply advanced analytics that can effectively understand
customers’ behaviour at the individual level, so that the decision
of when to insert friction can be accurately taken. To enable
consumer choice of authentication mechanism, the bank also
must have a exible range of authenticators available. To that end,
many of the FIs on the forefront of this movement are approaching
the process in a phased manner and either building or buying the
requisite building blocks:
Data lake: Many FIs on this journey are standing up their own
bespoke data environment for orchestration (as well as other
real-time fraud needs) or streaming the data directly into the risk
engine, since data currency is important to eectively analyse
the segment-of-one customer behaviour.
Advanced analytical engine: Orchestration requires advanced,
machine-learning based models that can baseline behaviour
for individual customers, and then understand when their
transactional activity deviates from the norm, thus requiring
stepped-up authentication.
Authentication hub: In order to provide a range of authenti-
cation options to customers, FIs are turning to platform-based
authentication hubs that provide a range of authentication
options, and make it easier for the FI to swap in new authentica-
tors on an ongoing basis.
A handful of large FIs already have their initial iteration of orche-
stra tion in production, and 2019 will see more joining these ranks.
Among those leading the way, there is a strong belief that the
resulting enhancements to the customer journey will not only
improve the bottom line, but will also prove to be a competitive
dierentiator over time.
The concept of open banking promises users greater control over
their nancial data; however, it is not without risks, and its success
is tied to consumer confidence when it comes to the security
and privacy of their information. Indeed, ahead of the arrival of
open banking in the UK, a 2017 Accenture survey of more than
2,000 British consumers found that two-thirds were not prepared
to share their personal nancial data with thirdparty providers.
As Accenture’s managing director Jeremy Light commented at the
time, “Open banking has the potential to transform customers’
relationship with nancial products, but it hinges on consumers’
willingness to embrace it.”
Privacy concerns regarding the practice of “screen scraping”
where a thirdparty payment or nancial data aggregation ser
vice accesses bank accounts on the consumer’s behalf using
their credentials – were surfaced by Barclays’ managing director
Catherine McGrath in response to the news of banking giant
HSBC’s foray into open banking with its aggregate app. The HSBC
application pulled financial data from different bank accounts
into one place for users. “With screen scraping, you have to give
someone login details and then they can see absolutely every thing;
you don’t have the ability to discriminate to say just six months’
worth of transactional data,” Ms McGrath said. “Our view is the
best way for customers to share their data through APIs, so they
are in charge of their data.”
Regulatory implications and limitations
Around the world, regulations are emerging in line with the growing
trend towards open banking. A prominent example is the second
Payment Services Directive (PSD2), which came into effect in
Europe at the start of 2018. PSD2 is being closely watched by
other markets as open banking gains momentum, and regulated
service providers navigate concerns regarding the implications for
user privacy and security.
Whether or not these concerns ultimately slow Europe’s adoption
of open banking largely depends on how the Strong Customer
Authentication requirements dened in the PSD2 Regulatory Tech
ni cal Standard are enforced. To help ensure successful adoption
of open banking, the FIDO Alliance has taken an active role in
helping European regulators and API design groups understand
how standards-based, modern authentication can be used to
deprecate today’s screen scraping practices while enabling a
timely and secure migration to the open banking API model.
It is critical that open banking is implemented via modern APIs
and protected by high assurance Strong Customer Authentication,
as only an API-centred model is capable of protecting consumer
privacy by providing granular access controls enabling the
consumer to determine how much of their data is shared with any
given third-party service provider. And only modern cryptographic-
based authentication is fundamentally resistant to today’s most
common and effective account compromise attacks, such as
phishing for passwords and even one-time-passcodes (OTP).
FIDO Alliance
Open Banking: Why a New Approach to Authentication Is Key to its Success
Brett McDowell | Executive Director | FIDO Alliance
About Brett McDowell: Brett McDowell helped establish the FIDO Alliance in 2012 to remove the world’s
dependency on passwords through open standards for strong authentication. Previously, he was head
of ecosystem security at PayPal, where he developed strategies to improve online customer security.
About FIDO Alliance: The FIDO Alliance works to
address the lack of interoperability among strong authen-
tication technologies and to remedy the problems users
face managing multiple passwords. The Alliance is
changing the nature of authentication with standards for
simpler, stronger authentication that dene an open,
scalable, interoperable set of mechanisms which reduce
reliance on passwords.
Bolstering security, privacy, and usability with device-
based authentication
New and improved methods of authentication are now available
through open industry standards from the FIDO Alliance and
W3C. Collectively known as FIDO Authentication, this innovative
technology leverages on-device user verification such as the
biometric capabilities on our mobile phones and combines this
with interoperable protocols for strong cryptographic authenti-
cation. Biometrics is a compelling proposition for banks and
other nancial services companies, due to their ability to perform
without dependency on the user remembering or sharing a
password, greatly enhancing customer security while improving
the user’s authentication experience.
In practice, by utilising public key cryptography techniques in
com bi nation with “one touch” biometrics and/or security keys,
the proliferation of smart devices can be used to provide stronger
authentication without burdening users. If the customer uses
their ngerprint, face, or PIN code to unlock their device, banks
can now combine that same user verication method with strong
cryptographic protocols made available through on-device plat-
form APIs, including a Javascript API for web apps. This would
allow customers to securely access their accounts online in full
compliance with PSD2 strong customer authentication require-
ments, on both apps and websites.
Complying with SCA requirements – our approach
FIDO certication provides a clear path for nancial services orga
ni sations to comply with PSD2 strong customer authentication
The FIDO Alliance’s authentication standards provide a scalable
way for the European nancial ecosystem to meet PSD2 require
ments for strong authentication of user logins and cryptographically
signed transactions, while also meeting organisational and con-
sumer demand for transaction convenience. FIDO certification
pro grammes oer an independent validation of implementations
conformance, interoperability, security, and even biometric perfor -
mance when applicable. All certified devices are eligible to
be listed in a public registry of device metadata that enables a
nancial service to evaluate the security properties of the device,
ensuring the device’s ability to comply with the restricted operating
environment requirements detailed in the PSD2 RTS.
PSD2 should signicantly improve the way thirdparties access
account data. Ultimately, public trust is essential for momentum to
continue to build around open banking and to ensure its enduring
success. In order to build and maintain this condence, a new
approach to authentication must be taken in which there are adop-
ted far superior modern methods that will enhance security and
usability to the benet of all concerned.
Customer Onboarding and Digital Identity
Customer Onboarding and Identity
Mirela Ciobanu | Senior Editor | The Paypers
Did you know that 59% of customers looking to open a bank account have walked away from online applications
in the last 12 months? The reason behind this: many application processes aren’t really designed for the digital age.
However, the good news is that smart ntech businesses and challenger banks are getting under the skin of digital iden-
tity and using our uniqueness to unlock a frictionless future. They do so by tapping into technology such as behavioural
biometrics, machine learning and articial intelligence, and lately also blockchain to support secure, intuitive and perso-
na lised digital experiences that are benecial for both companies and consumers alike.
In this chapter, we will see how the onboarding process looks like, not only from a customer’s perspective working with
a nancial services institution (FI) or other regulated entities, but also from a FI’s perspective onboarding new clients.
Banks are looking for ways to increase conversion of new customers applying for their product/service, be relevant for them,
while also managing risks associated with KYC/onboarding processes. But customers are demanding a exible (mobile
rst) and modular onboarding process, and regulators are constantly watching the market and updated/adopt new
regulations (e.g. AMLD5).
Will banks be able to get this puzzle right, in time? After all, improving the customer onboarding experience should be a
priority for nancial institutions, especially since regulations such as PSD2 will enable customers to change their nancial
service provider more easily.
Onboarding new customers in a digital world: a bank’s perspective
After a few years of battles between incumbent banks and smart ntechs/challengers, everyone has agreed that digital
customers need digital processes. Nowadays, for many nancial services organisations, the onboarding process is
considered costly, prone to fraud and creates unnecessary friction in the customer’s experience. This old approach is simply
not sustainable as it gives rise to high abandon rates and does not meet the expectations of a younger digitally ‘native’
How is my current onboarding process performing? The incumbents
Because many application processes aren’t really designed for the digital age, incumbent banks just replicate traditional
onboarding processes, pushing only some parts of it online. As a result, up to half of digital applicants can’t actually
complete an application online; instead, they have to go into a branch to verify their identities, or submit additional
An Introduction to Customer Onboarding
and Digital Identity Verication
In 2016, Signicat conducted a research called the Battle to On-board that aimed to portray the onboarding processes for
the UK nancial services consumers. The research found that 40% of consumers had abandoned bank applications;
more than 1 in 3 (39%) abandonments were due to the length of time taken and a third (34%) were due to demanding too
much personal information. Interestingly, the company performed the same research two years later and the results
were similarly devastating for banks. In fact, it was worse than ever in the UK, with 56% of respondents having
abandoned an application. Among other impediments for applying cited by consumers were the fact that they had to
provide personal information by post or take it into the branch, and sometimes the language used by the bank was
Nevertheless, some progress has been made with banks such as China Merchant Bank, one of the largest credit card
companies in China, Wells Fargo and the Bank of America that have reached out to AI assistants to improve customer
experience. For instance, Bank of America’s ‘Erica’ chatbot was designed to maximise the opportunities of the
growing demand for mobile banking and is capable of anticipating the nancial needs of each individual customer and
sending them personal smart recommendations to help them achieve their nancial goals.
In Europe, most innovative banks such as ABN AMRO, CaixaBank and BBVA have developed their own hassle-free
banking brands to cater for millennials and digital savvy users. For instance, in Spain, CaixaBank launched in 2016
imaginBank, a mobile banking service that enables users to control their nances, view their account securely within
Facebook, or draw money from an ATM without a card and send money to friends using only a mobile number. Similarly,
present in the Netherlands, Germany, Belgium and Austria, Moneyou, a brand of ABN AMRO, is a mobile banking service
connected to a mobile app called Tikkie. The app can be used by anyone, regardless of who they bank with; it is only
necessary that the person receiving the money to have the app. Once the users enter their name, mobile phone number
and the IBAN number, they can start sending payment requests via WhatsApp, Facebook Messenger, Telegram, QR-code
or text (SMS).
How is my current onboarding process performing – the challengers
Even from the rst encounter with the clients, challengers have been praised for providing great user experience. And
why is that? They are digital, they can develop from scratch, have smaller product oering, they do not depend on
legacy systems, and are adopting new technologies to automate identity verication processes.
For example, Fidor Bank, a German online bank, founded in 2009, has a simplied, threestage process of onboarding
depending on two essential variables: customer behaviour and product complexity. For the Fidor’s Smart Cash Account
product, the entry point for a new customer is to join the Fidor community, by supplying one’s credentials from Facebook,
with no obligation to buy anything. Step two is obtaining a pre-funded online ‘wallet’ that can be used to move money
within a closed loop as the user graduates to being a ‘customer’ after passing reduced KYC. This allows him or her
to test out Fidor, again without any further commitment, while still being part of the community. The third and last
step is to open a more traditional account after passing full KYC. Now the customer can also trade commodities, FX, and
digital currencies.
An Introduction to Customer Onboarding
and Digital Identity Verication
So, the Fidor Smart Cash Account behaves according to the way the customer registers, not according to a bank-
imposed process.
In general, banks must check the identity of everyone opening an account to prevent money laundering or other criminal
nancing activities. While these ID checks used to take place exclusively at bank counters, nowadays many services use
video identication customers rotate their ID card in front of a camera allowing sta to check for security features, like
holograms  or just seles.
However, this simplicity might come at a cost. Germany’s N26 could be potentially vulnerable to money laundering
and terrorism nancing, according to a German publication WirtschaftsWoche, which exposed a security gap at the online
banking startup. As the ntech rolled out a sele validation procedure for account opening, it is easier for criminals to open
accounts with fake IDs. A WirtschaftsWoche correspondent saw how a man scanned a friend’s ID, added his own passport
photo to the ID, printed it out and stuck it atop of a white plastic card that was the same size as the oce ID card in his
country. He cut the edges to make them round and the result was a new identication card that could be used to open a
new bank account.
“Go online or go home” – ways to improve it
INNOPAY developed a Benchmark that provides banks with essential insights into how to make a good first
impression on customers. INNOPAY consultants have identied six key actions that banks should execute in order to
provide the prospective customers the best-possible onboarding experience and increase conversion rates.
1. Eliminate all channel breaks to support an end-to-end fully digital onboarding experience. For example, banks should
adopt paperless onboarding processes as well as processes for which no physical signature is required.
2. Make required onboarding information and prerequisites transparent and understandable for the user. For instance,
clear information and communication are key, so that the potential customer has all relevant details at hand and can
run through the process in a smooth way.
3. Guide the customer through the onboarding ow and empower customer support to help prospects during onboarding
in a quick and high-quality manner. The end result is that the prospects always know where they are currently positioned
within the process and nd information quickly. If they do not understand why the bank is asking for certain information or
why the bank requires the prospect to use a certain identication method, they can rely on professional support provided
by the bank.
4. Make use of tools that ease the process of data entry and eliminate errors. Thus, errors can be prevented by various
inprocess validation tools to increase conversion and also to reduce manual eorts by the bank, leading to cost
5. Enable customers to instantly login and start using the payment account after a successful onboarding.
6. Deliver a consistent look and feel throughout the whole onboarding experience.
An Introduction to Customer Onboarding
and Digital Identity Verication
Overall, we can conclude that banks can stay relevant for their customers if they transform the entire on-boarding process
online. So far, we have seen that consumers are more likely to apply for a product if the process is 100% online and if
paper-based identity checks are eliminated.
Moreover, the onboarding process could be accelerated if they could use their veried physical ID, such as a passport
or driving license, and here, in the 100%online application process, an important role is played by identity verication.
Identity verication: some last thoughts
Identity verication is proving that specic identity attributes are actually connected to the person, entity, or thing that
they are intended to represent. According to Josje Fiolet, Digital Onboarding lead at INNOPAY, video identication,
reading the chip of the document via NFC (Near-Field Communication), using eID solutions, or taking a picture of the
ID document can enable businesses to answer questions such as ‘Is the customer’s document valid?’, or ‘Is the person
really who he/she claims to be?’.
To build a reliable prole of the customer, other techniques can also be considered. The trail of data that we leave behind
may not be an identication method in itself, but it can serve as an additional step when building a trustworthy prole. For
example, our activity on social networks can be used to provide a certain level of assurance of someone’s identity, and
the account’s prole picture can be matched with the picture in the identication document.
For eective client identication, a business must have access to a range of technology solutions that can indicate
the veracity of an individual along with providing access to worldwide trusted datasets that contain billions of data
elements of information from governments/public bodies, including global postal, telecoms and other public data, to
validate the underlying data associated with nancial services provision. Not only does this deliver a 360degree view of
the individual, but it also authenticates who they are.
The key to all these lies in balancing these elements in order to create perfectly tailored products. By understanding
the unique needs of customers, nancial businesses can help governments and major institutions ght fraud and grant
access to underserved and legitimate customers. We can conclude by underlying one of Money 2020’s ideas from the
2018 edition: once we solve this puzzle of identity custodianship, we can craft a masterpiece in which uniqueness is
celebrated, protected and used responsibly.
An Introduction to Customer Onboarding
and Digital Identity Verication
How would you describe Melissa for those who are
not familiar with the company?
Melissa is a leading provider of global identity verication solu
tions, utilising innovative technology to provide our clients with
a data-driven competitive advantage and enhanced Know Your
Customer (KYC) and Anti-Money Laundering (AML) processes to
help combat fraud.
How can nancial institutions that are looking to com ply
with AML take ad van tage of Melissa’s services to
deliver on the customer’s expectation for convenience,
speed and simplicity, while also mitigating the risk of
21st century customers expect quick and secure nancial service
provision – if the consumer experience is poor, they will move
to another provider who can deliver a better outcome. Melissa
offers a range of global identity verification solutions that are
easily integrated into existing customer service platforms and IT
systems. Melissa’s solutions range from ‘proof of address’ check
to full bio metrics that authenticate customers in real-time. Using
Melissa enables organisations to retire costly legacy systems,
reduce headcount for manual review, and avoid reputational risk.
Regulatory AML checks are completed in a fraction of a second,
where manual review could take days to complete. By using
Melissa, access to global identity data, sanctions and watchlists
are one click away – speeding the processing of applications.
AML Screening is an important step in determining the risk of
an individual, to make sure business is not being con duc ted
with those committing money laundering or nancing terrorism.
Melissa screens against global sanctions and PEP checks
(Politically Exposed Person), a database containing information on
world leaders for 200+ countries.
A cornerstone of global anti-money laundering
controls are the KYC processes/requirements. How
does Melissa perform such processes?
Melissa can quickly perform KYC through our ID Verification
solutions (IDV), providing access to global datasets containing
billions of trusted identity elements from the government, global
postal, telecoms data and other data sources in real-time. The
underlying data provided at input can be cross-checked, building
a confidence score for the applicant based on strength of the
underlying data. Melissa helps further by identifying individuals at
the ‘point of entry’ via imaging and facial recognition technology.
Barley Laing | Managing Director | Melissa Global Intelligence
About Barley Laing: Barley Laing is Managing Director at Melissa Global Intelligence, where he leads
commercial and operational activities, helping the organisation to become a global leader in identity and
data verication services. Previously, Barley was CEO of World Address and 2L Technologies, and has
held senior positions at Xerox, British Telecom, ADC and Shell.
Melissa Global Intelligence
The Paypers sat down with Barley Laing, the Managing Director of Melissa Global Intelligence, to discuss the latest ID
verication and KYC trends and developments in the nancial industry.
21st century customers
expect quick and secure nancial
service provision. Melissa’s wide
range of global identity solutions
authenticate customers in real-
time so organisations don’t have
to compromise the customer’s
experience while mitigating the
risk of fraud.
Click here for the company profile
As articial intelligence helps brands engage with consumers
more eciently, it could evolve to play a role in ID verication
in a way that helps brands to deliver a seamless customer
The role of behavioural biometrics in ID verication will grow and
evolve. This could include monitoring how people type on the
keyboard and use the mouse or touchscreen. It could become
an important way to authenticate an ID.
Augmented intelligence will play a key role along with articial
intelligence, working to enhance human intelligence. For identity
verication, it will mean not only smarter intelligence, but also
stronger intelligence.
In the shorter term:
Growth in facial recognition technology will conrm ID.
As consumers increasingly worry about their ID being stolen,
there will be a strong evolution in technology that veries and
protects customer data, as brands seek to placate their fears.
Fraud is a growing global issue, we see IDV becoming the norm
across all sectors and service provision beyond nancial ser
This is done by checking the applicant by cross-referencing a live
image (biometric facial recognition of a sele) against a scanned
ID document image (eg driver license photo). ID documents are
validated to ensure they are not fake, and the held data uplifted
via Optical Character Recognition (OCR) to avoid mistakes being
made at application.
How would you explain the dierence between eec
tive client identication and poor KYC standards?
This dierence can be categorised depending on perspective:
Consumers want a slick application process. If the supplier
organisation can quickly establish a customer’s ID, the consumer
will have condence in that provider.
Financial Service Organisations with poor KYC processes can
lose customers at application, but this could also lead to fraud
and compliance issues that will impact their brand and bottom
line. Using modern KYC initiatives effectively can mean better
sales and increased customer engagement.
Fraudsters actively target organisations with poor KYC processes,
they know less eective ID resolution means easier victims.
Your product package includes a solution that
addresses ID verication that gathers data in order
to complete People Data. As sometimes not all
gathered data is useful, how does your solution
maximise the value of this data?
Research shows many ID checks fail from incorrect data entry,
organisations can waste money by running ID checks that are
destined to fail because the basic data veracity was not conrmed
rst. Melissa’s solution ensures underlying data is correct before
per forming the ID check, this happens in fractions of a second
and without disruption to the customer. Having a complete and
vali dated identity record of a customer means that organisations
will better communicate, and can complete transactions with their
client base in condence, maximising the value of their customer
Can you identify possible trends in ID verication?
And what can we expect in the next ve years?
In the next ve years I expect that:
About Melissa Global Intelligence: Melissa delivers
exible, real-time technology solutions for global identity
verification and entity resolution. Since 1985, more
than 10,000 global customers including banks, credit
unions, mortgage lenders and payment providers have
relied on Melissa to verify an individual’s identity with
our best-of-breed solutions for global address parsing
and verication, and advanced matching algorithms
to minimize risk and fraud.
At the turn of this decade, the “GDP of the internet” began rising
precipitously; online merchants, particularly micro-merchants,
began opening online storefronts in increasing numbers. Yet the
technology powering the flow of money online was simply not
keeping pace. It was this set of unique circumstances that necessi-
tated the creation of a new generation of payment solutions. With
their elegantly simple code and their vast network of relationships
with credit card issuers, banks and financial services, these
payment solutions open the doors to a truly borderless market-
place where online merchants and buyers could transact freely.
A layer of trust
There was, however, another problem that stood in the way: If these
payment solutions wanted to enter new markets, particularly
un chartered and unfamiliar ones, they needed to rst build a layer
of trust between themselves and their new customers – the online
This layer of trust needs to be built on:
Customer due diligence (CDD): Ensuring a level of CDD that is
commensurate with the risks involved in transacting with new
cus tomers in these regions. For payment companies, banks,
and nancial services providers, this includes meeting regulatory
requirements such as Know Your Customer (KYC), Anti
Money Laundering (AML).
Fraud prevention: While the digital economy has created unpre
cedented opportunities for both established and upstart mer-
chants around the world, it is also prone to fraud. Indeed, preven-
tion is the operative word here, because very often fraud is only
detected after the fact.
The challenge
As it happens, the success of both CDD and fraud prevention
hinge on a critical process: Identity verication. When it comes
to highly competitive and fast-growing companies, it becomes
imperative to move quickly and capture as much market share
as possible. For these companies, it becomes essential to have
an identity verication process that can scale quickly, eciently,
and costeectively. In order to do that, these companies need
access to a variety of trusted and reliable data sources; but, as
it happens, the data that is being sought to verify the identity of
mer chants in these markets is often available exclusively with
local data vendors.
Consider a growing payments company; let’s say it is foraying into
the Peruvian market. It will likely struggle to forge relationships
with local data partners there; it would have to sign multiple
contracts with multiple data partners in order to gain access to
a suciently large swathe of identity data. This process requires
a great deal of time, resources and familiarity with the local eco-
system; identifying, procuring, and vetting data sources, and then
manually undertaking security and compliance checks. Even from
a technology standpoint, the time and investment required to build
an API for every data source that the company intends to tap into,
become critical roadblocks to their expansion plans. Given these
constraints, it would take anywhere between six months to a year
for these companies to integrate each data source onto their
systems. Now, consider the total time it would take to integrate
with multiple data sources across multiple countries; that’s when
the project begins to look unfeasible.
Hard Problems: Identity Verication, Fraud Prevention and the Giant Leap Towards
Financial Inclusion
Zac Cohen | General Manager | Trulioo
About Zac Cohen: Zac Cohen is a versatile leader experienced in managing and scaling high-growth
companies. Zac is currently the General Manager at Trulioo – a hyper-growth Vancouver startup solving
global identity challenges associated with international regulatory compliance, fraud prevention, and
trust and safety online. He is passionate about fostering change-makers who want to make an impact
and are engaged in building groundbreaking solutions to solve our world’s most pressing problems.
Click here for the company profile
About Trulioo: Trulioo is a global identity verification
company providing advanced analytics from traditional
and alternative data sources to verify identities in real-
time. Through GlobalGateway, Trulioo’s electronic
verication platform, clients are able to streamline their
cross-border compliance needs, helping them meet
Anti-Money Laundering and Know Your Customer
requirements, while simultaneously mitigating fraud and
reducing risk.
The solution: a single API to access identity data
across the world
Trulioo has, to a large extent, mitigated this problem; as one of the
world’s preeminent identity verication solutions, we have access
to hundreds of data sources. Through a single API, GlobalGateway
 Trulioo’s agship solution  provides secure access to over 400
data sources across the world. With GlobalGateway, our clients
no longer need to sign multiple contracts with multiple parties;
instead, a single contract with Trulioo provisions it with access to
data from multiple data partners. In fact, one of the world’s leading
cross-border payroll solutions uses GlobalGateway to verify the
identity of payees in 52 countries across different continents,
including Chile, Jordan and Egypt.
Instant access to a plethora of data sources also goes a long way
in mitigating risk; for instance, companies tend to put o their CDD
process till such time as a merchant starts transacting beyond a
certain dollar threshold — this is mainly because traditional pro-
cesses of identity verication were manual, slow and required much
human eort. The instantaneity of identity verication, which Trulioo
enables, allows companies to place identity verification at the
very beginning of merchant onboarding; the same instantaneity
makes it easy for many of our clients to verify (rather, reverify) the
identities of their existing merchants. As a result, our clients are
able to understand their entire consumer base quickly and take
timely cognizance of any risks that their merchants might pose.
Mobile ID verication: a boost for nancial inclusion
and an antidote to fraud prevention
From very early on, we, at Trulioo, saw identity verication as a
catalyst for nancial inclusion; to that end, we realised that we
needed to cover hard-to-reach areas, which lacked traditional
sources of identity data. As of October, Trulioo can verify the
identity of up to ve billion people, or two-thirds of the world’s
population, along with 250 million businesses, including micro-
merchants. In developing areas of the world, where a large part of
the population is “unbanked”, and traditional sources of identity
data have limited coverage, mobile network operators (MNOs)
can play a game-changing role. In developing markets, the mobile
user base outstrips that of nancial services: for instance, over the
last four years, over a billion mobile accounts were opened around
the world, compared to 500 million bank accounts. Indeed, the
data in possession of MNOs can go a long way in verifying the
identity of otherwise “thinle” merchants.
To that end, we began partnering with MNOs around the world.
Currently, we have access to identity data provided by dozens of
MNOs, which cover 1.8 billion mobile users. When the traditional
KYC-compliant sources of data are combined with MNO data,
one is able to obtain more insight into the identity that one is trying
to verify. No less important is the added value that MNOs bring to
fraud prevention; for example, when verifying a merchant’s mobile
number against MNO data, GlobalGateway can ag numbers that
are VoIP numbers, which are often prone to misuse by fraudsters.
We are one breakthrough away from nancial inclusion
If we look back at the evolution of online commerce, we realise
that at dierent points, there have been dierent technological
breakthroughs that have catalysed the sector in dierent ways.
The revolution in online payments was one such breakthrough;
identity verication is on the cusp of being the next breakthrough.
Today, merchants from around the world can transact online as
free agents of the online economy; our dream is to see a world
where they are able to transact not just as free agents but equals
of a nancially inclusive ecosystem.