Bart Baesens & Veronique Van Vlasselaer 
Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques [PDF ebook] 
A Guide to Data Science for Fraud Detection

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. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention.

It is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak.

* Examine fraud patterns in historical data

* Utilize labeled, unlabeled, and networked data

* Detect fraud before the damage cascades

* Reduce losses, increase recovery, and tighten security

The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.

Jadual kandungan

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

Mengenai Pengarang

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 Ph D 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.