Fraud Analytics: Using Descriptive, Predictive, and Social Network Techniques
By Bart Baesens, Veronique Van Vlasselaer and Wouter Verbeke
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Fraud Analytics: Using Descriptive, Predictive, and Social Network Techniques
Description
The sooner you detect fraud, the more likely you are to reduce losses, increase recovery and tighten security. Detecting fraud at an early stage, though, is more difficult than detecting it later and requires specific techniques.
"Fraud Analytics" provides you with a comprehensive overview of fraud detection analytical techniques and provides implementation guidance for an effective fraud prevention solution to help you detect fraud early.
You will learn how to:
- Detect fraud sooner
- Examine fraud patterns in historical data
- Use labeled, unlabeled and networked data
Topics covered include:
- The fraud analytics process model
- Big data
- Anomaly detection
- Neural networks
- Social network metrics
- Community mining
- Visual analytics
Product Details
| Label | Value |
|---|---|
| ISBN | 978-1-119-13312-4 |
| Publisher | John Wiley & Sons |
| Published | Copyright 2015 |
| Pages | 400 |
| Format | Hardcover |
Table of Contents
| List of Figures | |
|---|---|
| Foreword | |
| Preface | |
| Chapter 1 | Fraud: Detection, Prevention and Analytics! |
| Chapter 2 | Data Collection, Sampling and Preprocessing |
| Chapter 3 | Descriptive Analytics for Fraud Detection |
| Chapter 4 | Predictive Analytics for Fraud Detection |
| Chapter 5 | Social Network Analysis for Fraud Detection |
| Chapter 6 | Fraud Analytics: Post-Processing |
| Chapter 7 | Fraud Analytics: A Broader Perspective |
| Index |
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