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

ISBN: 9781119133124
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$52.00
SKU:
9781119133124
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25 units
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This title is Printed on Demand and is not stocked for immediate shipping.Please allow 10-15 business days for this title to print.

Product Details

Author:
Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke
Format:
Hardcover
Pages:
400
Publisher:
Wiley (August 17, 2015)
Language:
English
Audience:
General/trade
ISBN-13:
9781119133124
Weight:
21.6oz
Case Pack:
10
Series:
Wiley and SAS Business Series
File:
Wiley-wileyUS_2_1_20250715-20250715.xml
As low as:
$35.88
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Wiley
Shipping Origin:
Wiley-1
List Price:
$52.00
Publisher Identifier:
P-WIL
Discount Code:
D
Dimensions:
6.2" x 9.1" x 1.5"
Print on Demand (longer leads times apply):
Country of Origin:
United States
Pub Discount:
50
Imprint:
Wiley

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Overview

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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