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. 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. Chapter 1: Fraud: Detection, Prevention & 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 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 Clustering One Class SVMs References 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 Networks: Form, Components, Characteristics and their Applications Is Fraud a Social Phenomenon? An Introduction to Homophily Impact of the Neighborhood: Metrics Community Mining: Finding Groups of Fraudsters Extending the Graph: Towards a Bipartite Representation Case Study: GOTCHA! References Chapter 6: Fraud Analytics: Post Processing Introduction The Analytical Fraud Model Lifecycle Model Representation Selecting the Sample to Investigate Fraud Alert and Case Management Visual Analytics Backtesting Analytical Fraud Models Model Design and Documentation References Chapter 7: Fraud Analytics: A Broader Perspective Introduction Data Quality Privacy Capital Calculation for Fraud Loss An Economic Perspective on Fraud Analytics In- Versus Outsourcing Modeling Extensions The Internet of Things Corporate Fraud Governance