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

Baesens, Bart/ Verbeke, Wouter/ Van Vlasselaer, Veronique

ISBN 10: 1119133122 / ISBN 13: 9781119133124

Published by John Wiley & Sons Inc, 2015

About this Item

1st edition. 400 pages. 9.06x6.38x1.42 inches. In Stock. This item is printed on demand.

Seller Inventory # __1119133122

Bibliographic Details

• Title: Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques A Guide to Data Science for Fraud Detection
• Publisher: John Wiley & Sons Inc
• Publication Date: 2015
• Language: English
• ISBN 10: 1119133122
• ISBN 13: 9781119133124
• Binding: Hardcover
• Condition: Brand New
• Weight: 0.54 kilograms

About this title

Synopsis

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.

About the Author

Professor Bart Baesens is a professor at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom).  He has done extensive research on big data & analytics, customer relationship management, web analytics, fraud detection, and credit risk management. His findings have been published in well-known international journals (e.g. Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, Journal of Machine Learning Research, ...) and presented at international top conferences.  He is also author of the books Credit Risk Management: Basic Concepts (goo.gl/T6FNOn) , published by Oxford University Press in 2008; and Analytics in a Big Data World (goo.gl/k3kBrB), published by Wiley in 2014.  His research is summarized at dataminingapps.com.  He also regularly tutors, advises and provides consulting support to international firms with respect to their analytics and credit risk management strategy.