Cover -- Title Page -- Copyright -- Contents -- List of Figures -- Foreword -- Preface -- Acknowledgments -- Chapter 1 Fraud: Detection, Prevention, and Analytics! -- Introduction -- Fraud! -- Fraud Detection and Prevention -- Big Data for Fraud Detection -- Data-Driven Fraud Detection -- Fraud-Detection Techniques -- Fraud Cycle -- The Fraud Analytics Process Model -- Fraud Data Scientists -- A Fraud Data Scientist Should Have Solid Quantitative Skills -- A Fraud Data Scientist Should Be a Good Programmer -- A Fraud Data Scientist Should Excel in Communication and Visualization Skills -- A Fraud Data Scientist Should Have a Solid Business Understanding -- A Fraud Data Scientist Should Be Creative -- A Scientific Perspective on Fraud -- References -- 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 -- Introduction -- Graphical Outlier Detection Procedures -- Statistical Outlier Detection Procedures -- Break-Point Analysis -- Peer-Group Analysis -- Association Rule Analysis -- Clustering -- Introduction -- Distance Metrics -- Hierarchical Clustering -- Example of Hierarchical Clustering Procedures -- k-Means Clustering -- Self-Organizing Maps -- Clustering with Constraints -- Evaluating and Interpreting Clustering Solutions -- One-Class SVMs -- References -- Chapter 4 Predictive Analytics for Fraud Detection -- Introduction.

Target Definition -- Linear Regression -- Logistic Regression -- Basic Concepts -- Logistic Regression Properties -- Building a Logistic Regression Scorecard -- Variable Selection for Linear and Logistic Regression -- Decision Trees -- Basic Concepts -- Splitting Decision -- Stopping Decision -- Decision Tree Properties -- Regression Trees -- Using Decision Trees in Fraud Analytics -- Neural Networks -- Basic Concepts -- Weight Learning -- Opening the Neural Network Black Box -- Support Vector Machines -- Linear Programming -- The Linear Separable Case -- The Linear Nonseparable Case -- The Nonlinear SVM Classifier -- SVMs for Regression -- Opening the SVM Black Box -- Ensemble Methods -- Bagging -- Boosting -- Random Forests -- Evaluating Ensemble Methods -- Multiclass Classification Techniques -- Multiclass Logistic Regression -- Multiclass Decision Trees -- Multiclass Neural Networks -- Multiclass Support Vector Machines -- Evaluating Predictive Models -- Splitting Up the Data Set -- Performance Measures for Classification Models -- Performance Measures for Regression Models -- Other Performance Measures for Predictive Analytical Models -- Developing Predictive Models for Skewed Data Sets -- Varying the Sample Window -- Undersampling and Oversampling -- Synthetic Minority Oversampling Technique (SMOTE) -- Likelihood Approach -- Adjusting Posterior Probabilities -- Cost-sensitive Learning -- Fraud Performance Benchmarks -- References -- Chapter 5 Social Network Analysis for Fraud Detection -- Networks: Form, Components, Characteristics, and Their Applications -- Social Networks -- Network Components -- Network Representation -- Is Fraud a Social Phenomenon? An Introduction to Homophily -- Impact of the Neighborhood: Metrics -- Neighborhood Metrics -- Centrality Metrics -- Collective Inference Algorithms -- Featurization: Summary Overview.

Community Mining: Finding Groups of Fraudsters -- Extending the Graph: Toward a Bipartite Representation -- Multipartite Graphs -- Case Study: Gotcha! -- References -- Chapter 6 Fraud Analytics: Post-Processing -- Introduction -- The Analytical Fraud Model Life Cycle -- Model Representation -- Traffic Light Indicator Approach -- Decision Tables -- Selecting the Sample to Investigate -- Fraud Alert and Case Management -- Visual Analytics -- Backtesting Analytical Fraud Models -- Introduction -- Backtesting Data Stability -- Backtesting Model Stability -- Backtesting Model Calibration -- Model Design and Documentation -- References -- Chapter 7 Fraud Analytics: A Broader Perspective -- Introduction -- Data Quality -- Data-Quality Issues -- Data-Quality Programs and Management -- Privacy -- The RACI Matrix -- Accessing Internal Data -- Label-Based Access Control (LBAC) -- Accessing External Data -- Capital Calculation for Fraud Loss -- Expected and Unexpected Losses -- Aggregate Loss Distribution -- Capital Calculation for Fraud Loss Using Monte Carlo Simulation -- An Economic Perspective on Fraud Analytics -- Total Cost of Ownership -- Return on Investment -- In Versus Outsourcing -- Modeling Extensions -- Forecasting -- Text Analytics -- The Internet of Things -- Corporate Fraud Governance -- References -- About the Authors -- Index -- EULA.