Wiley And SAS Business Series : Fraud Analytics Using Descriptive, Predictive, Social Network Techniques A Guide To Data S (Wiley Series) Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke Analyti
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- Cover
- Title Page
- Copyright
- Contents
- List of Figures
- Foreword
- Preface
- Acknowledgments
- Chapter 1 Fraud: Detection, Prevention, and Analytics!
- Chapter 2 Data Collection, Sampling, and Preprocessing
- Introduction
- Types of Data Sources
- Merging Data Sources
- Sampling
- Types of Data Elements
- Visual Data Exploration and Exploratory Statistical Analysis
- Benford's Law
- Descriptive Statistics
- Missing Values
- Outlier Detection and Treatment
- Red Flags
- Standardizing Data
- Categorization
- Weights of Evidence Coding
- Variable Selection
- Principal Components Analysis
- RIDITs
- PRIDIT Analysis
- Segmentation
- References
- Chapter 3 Descriptive Analytics for Fraud Detection
- Chapter 4 Predictive Analytics for Fraud Detection
- Introduction
- Target Definition
- Linear Regression
- Logistic Regression
- Variable Selection for Linear and Logistic Regression
- Decision Trees
- Neural Networks
- Support Vector Machines
- Ensemble Methods
- Multiclass Classification Techniques
- Evaluating Predictive Models
- Other Performance Measures for Predictive Analytical Models
- Developing Predictive Models for Skewed Data Sets
- Fraud Performance Benchmarks
- References
- Chapter 5 Social Network Analysis for Fraud Detection
- Chapter 6 Fraud Analytics: Post-Processing
- Chapter 7 Fraud Analytics: A Broader Perspective
- About the Authors
- Index
- EULA