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Fraud detection is more valuable the sooner it is made, because further losses are prevented, potential recoveries are higher, and security issues can be addressed more rapidly, as such avoiding cascading damage to an organization. Detecting fraud in an early stage however is harder than detecting it in an evolved stage, and requires specific techniques discussed in this book. This book shows how analytics can be used to fight fraud by examining fraud patterns from historical data. It discusses the use of supervised learning (using a labeled data set), unsupervised learning (using an unlabeled data set), and social network learning (using a networked data set).  The techniques discussed can be applied 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. The authors provide a sound mix of both theoretical and technical insights, as well as practical implementation details.

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