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