Fraud analytics using descriptive, predictive, and social network techniques a guide to data science for fraud detection

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