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

Overview

The sooner fraud detection occurs the better—as the likelihood of further losses is lower, potential recoveries are higher, and security issues can be addressed more rapidly. Catching fraud in an early stage, though, is more difficult than detecting it later, and requires specific techniques. Packed with numerous real-world examples, Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques authoritatively shows you how to put historical data to work against fraud.

Authors Bart Baesens, Véronique Van Vlasselaer, and Wouter Verbeke expertly discuss the use of unsupervised learning, supervised learning, and social network learning using techniques across a wide variety of fraud applications, such as insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, and tax evasion. This book provides the essential guidance you need to examine fraud patterns from historical data in order to detect fraud early in the process.

Providing a clear look at the pivotal role analytics plays in managing fraud, this book includes straightforward guidance on:

• Fraud detection, prevention, and analytics
• Data collection, sampling, and preprocessing
• Descriptive analytics for fraud detection
• Predictive analytics for fraud detection
• Social network analytics for fraud detection
• Post processing of fraud analytics
• Fraud analytics from an economic perspective

Read Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques for a comprehensive overview of fraud detection analytical techniques and implementation guidance for an effective fraud prevention solution that works for your organization.

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