Login

Baesens, Bart

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

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

49,60€

Ebook, ePUB with Adobe DRM
ISBN: 9781119146834
DRM Restrictions

Printing120 pages with an additional page accrued every 7 hours, capped at 120 pages
Copy to clipboard5 excerpts

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.

Keywords: Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques, Bart Baesens, Wouter Verbeke, Veronique Van Vlasselaer, fraud detection, fraud prevention, fraud analytics solutions, early fraud detection, mitigating fraud damage, advanced fraud analytics, fraud analytic techniques, supervised learning techniques, unsupervised learning techniques, networked data learning, machine learning for fraud detection, fraud solutions, business informatics, practical fraud analytics, implementing anti-fraud strategies, Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques, Bart Baesens, Wouter Verbeke, Veronique Van Vlasselaer, fraud detection, fraud prevention, fraud analytics solutions, early fraud detection, mitigating fraud damage, advanced fraud analytics, fraud analytic techniques, supervised learning techniques, unsupervised learning techniques, networked data learning, machine learning for fraud detection, fraud solutions, business informatics, practical fraud analytics, implementing anti-fraud strategies, Business Technology

Author(s)
 
 
Publisher
John Wiley and Sons, Inc.
Publication year
2015
Language
en
Edition
1
Series
Wiley and SAS Business Series
Page amount
400 pages
Category
Information Technology, Telecommunications
Format
Ebook
eISBN (ePUB)
9781119146834
Printed ISBN
9781119133124

Similar titles