Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection

THE DEFINITIVE GUIDE TO THE DETECTION AND PREVENTION OF FRAUD THROUGH DATA ANALYTICS

Catch fraud early! Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques shows you how with a thorough overview of how to prevent losses and recover quickly as well as the security issues you need to address now. Exploring how auditors, corporate security prevention managers, and fraud prevention professionals can stay one step ahead of cyber criminals, this book addresses the different types of analytics in detecting fraud, including descriptive analytics, predictive analytics, and social network analysis.

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques offers a current, state–of–the–art detection and prevention methodology, describing the data necessary to detect fraud. Taking you from the basics of fraud detection data analytics, through advanced pattern recognition methodology, to cutting–edge social network analysis and fraud ring detection, this book presents essential coverage of:

The fraud analytics process model Big data Break point/peer group analysis Anomaly detection Linear/logistic regression Neural networks Ensemble methods Social network metrics Bipartite graphs Community mining Visual analytics Model monitoring and backtesting

Insightful and clearly written, this hands–on guide reveals what you need to know about fraud analytics and the secret to putting historical data to work in the fight against fraud.

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

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.