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Fraud analytics using descriptive, predictive, and social network techniques: a guide to data science for fraud detection

Fraud analytics using descriptive, predictive, and social network techniques: a guide to data science for fraud detection
Fraud analytics using descriptive, predictive, and social network techniques: a guide to data science for fraud detection
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.
Wiley
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Van Vlasselaer, Veronique
80a16e2b-f1d0-4d27-bec5-fe3669d9477c
Verbeke, Wouter
57c0d98a-130a-4202-b6dd-cdc6914f4732
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Van Vlasselaer, Veronique
80a16e2b-f1d0-4d27-bec5-fe3669d9477c
Verbeke, Wouter
57c0d98a-130a-4202-b6dd-cdc6914f4732

Baesens, Bart, Van Vlasselaer, Veronique and Verbeke, Wouter (2015) Fraud analytics using descriptive, predictive, and social network techniques: a guide to data science for fraud detection , Chichester. Wiley, 400pp.

Record type: Book

Abstract

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.

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Published date: August 2015

Identifiers

Local EPrints ID: 425730
URI: https://eprints.soton.ac.uk/id/eprint/425730
PURE UUID: 3e29bf4b-c65a-445d-90ef-4e4f6819898a

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Date deposited: 02 Nov 2018 17:30
Last modified: 02 Nov 2018 17:30

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