BART BAESENS is a full professor at KU Leuven, and a lecturer at the University of Southampton. He has done extensive research on analytics, customer relationship management, web analytics, fraud detection, and credit risk management. He regularly advises and provides consulting support to international firms with respect to their analytics and credit risk management strategy.

VÉRONIQUE VAN VLASSELAER is a PhD researcher in the Department of Decision Sciences and Information Management at KU Leuven. Her research focuses on the development of new techniques for fraud detection by combining predictive and network analytics.

WOUTER VERBEKE is an assistant professor at Vrije Universiteit Brussel (Brussels, Belgium). His research is situated in the field of predictive analytics and complex network analysis with applications in fraud, marketing, credit risk, human resources management, and mobility.

List of Figures xv
Foreword xxiii
Preface xxv
Acknowledgments xxix
Chapter 1 Fraud: Detection, Prevention, and Analytics! 1
Introduction 2
Fraud! 2
Fraud Detection and Prevention 10
Big Data for Fraud Detection 15
Data-Driven Fraud Detection 17
Fraud-Detection Techniques 19
Fraud Cycle 22
The Fraud Analytics Process Model 26
Fraud Data Scientists 30
A Fraud Data Scientist Should Have Solid Quantitative Skills 30
A Fraud Data Scientist Should Be a Good Programmer 31
A Fraud Data Scientist Should Excel in
Communication and Visualization Skills 31
A Fraud Data Scientist Should Have a Solid Business Understanding 32
A Fraud Data Scientist Should Be Creative 32
A Scientific Perspective on Fraud 33
References 35
Chapter 2 Data Collection, Sampling, and Preprocessing 37
Introduction 38
Types of Data Sources 38
Merging Data Sources 43
Sampling 45
Types of Data Elements 46
Visual Data Exploration and Exploratory Statistical Analysis 47
Benford's Law 48
Descriptive Statistics 51
Missing Values 52
Outlier Detection and Treatment 53
Red Flags 57
Standardizing Data 59
Categorization 60
Weights of Evidence Coding 63
Variable Selection 65
Principal Components Analysis 68
RIDITs 72
PRIDIT Analysis 73
Segmentation 74
References 75
Chapter 3 Descriptive Analytics for Fraud Detection 77
Introduction 78
Graphical Outlier Detection Procedures 79
Statistical Outlier Detection Procedures 83
Break-Point Analysis 84
Peer-Group Analysis 85
Association Rule Analysis 87
Clustering 89
Introduction 89
Distance Metrics 90
Hierarchical Clustering 94
Example of Hierarchical Clustering Procedures 97
k-Means Clustering 104
Self-Organizing Maps 109
Clustering with Constraints 111
Evaluating and Interpreting Clustering Solutions 114
One-Class SVMs 117
References 118
Chapter 4 Predictive Analytics for Fraud Detection 121
Introduction 122
Target Definition 123
Linear Regression 125
Logistic Regression 127
Basic Concepts 127
Logistic Regression Properties 129
Building a Logistic Regression Scorecard 131
Variable Selection for Linear and Logistic Regression 133
Decision Trees 136
Basic Concepts 136
Splitting Decision 137
Stopping Decision 140
Decision Tree Properties 141
Regression Trees 142
Using Decision Trees in Fraud Analytics 143
Neural Networks 144
Basic Concepts 144
Weight Learning 147
Opening the Neural Network Black Box 150
Support Vector Machines 155
Linear Programming 155
The Linear Separable Case 156
The Linear Nonseparable Case 159
The Nonlinear SVM Classifier 160
SVMs for Regression 161
Opening the SVM Black Box 163
Ensemble Methods 164
Bagging 164
Boosting 165
Random Forests 166
Evaluating Ensemble Methods 167
Multiclass Classification Techniques 168
Multiclass Logistic Regression 168
Multiclass Decision Trees 170
Multiclass Neural Networks 170
Multiclass Support Vector Machines 171
Evaluating Predictive Models 172
Splitting Up the Data Set 172
Performance Measures for Classification Models 176
Performance Measures for Regression Models 185
Other Performance Measures for Predictive Analytical Models 188
Developing Predictive Models for Skewed Data Sets 189
Varying the Sample Window 190
Undersampling and Oversampling 190
Synthetic Minority Oversampling Technique (SMOTE) 192
Likelihood Approach 194
Adjusting Posterior Probabilities 197
Cost-sensitive Learning 198
Fraud Performance Benchmarks 200
References 201
Chapter 5 Social Network Analysis for Fraud Detection 207
Networks: Form, Components, Characteristics, and Their Applications 209
Social Networks 211
Network Components 214
Network Representation 219
Is Fraud a Social Phenomenon? An Introduction to Homophily 222
Impact of the Neighborhood: Metrics 227
Neighborhood Metrics 228
Centrality Metrics 238
Collective Inference Algorithms 246
Featurization: Summary Overview 254
Community Mining: Finding Groups of Fraudsters 254
Extending the Graph: Toward a Bipartite Representation 266
Multipartite Graphs 269
Case Study: Gotcha! 270
References 277
Chapter 6 Fraud Analytics: Post-Processing 279
Introduction 280
The Analytical Fraud Model Life Cycle 280
Model Representation 281
Traffic Light Indicator Approach 282
Decision Tables 283
Selecting the Sample to Investigate 286
Fraud Alert and Case Management 290
Visual Analytics 296
Backtesting Analytical Fraud Models 302
Introduction 302
Backtesting Data Stability 302
Backtesting Model Stability 305
Backtesting Model Calibration 308
Model Design and Documentation 311
References 312
Chapter 7 Fraud Analytics: A Broader Perspective 313
Introduction 314
Data Quality 314
Data-Quality Issues 314
Data-Quality Programs and Management 315
Privacy 317
The RACI Matrix 318
Accessing Internal Data 319
Label-Based Access Control (LBAC) 324
Accessing External Data 325
Capital Calculation for Fraud Loss 326
Expected and Unexpected Losses 327
Aggregate Loss Distribution 329
Capital Calculation for Fraud Loss Using Monte Carlo Simulation 331
An Economic Perspective on Fraud Analytics 334
Total Cost of Ownership 334
Return on Investment 335
In Versus Outsourcing 337
Modeling Extensions 338
Forecasting 338
Text Analytics 340
The Internet of Things 342
Corporate Fraud Governance 344
References 346
About the Authors 347
Index 349

Detect fraud earlier to mitigate loss and prevent cascading damage
Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention.
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The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.