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Databases & Big Data - Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection (Wiley and SAS Business Series)

Description

Book 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.

Details

Discover the power of fraud analytics with our comprehensive guidebook, "Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection"! This authoritative resource is your key to detecting and mitigating fraud earlier, preventing cascading damage and saving your organization from significant revenue loss. With its in-depth coverage of various analytical techniques and expert insights into implementation, this book provides the ultimate framework for effective fraud prevention.

Don't let fraud jeopardize your organization's success. By utilizing the theory and technical aspects detailed in our guidebook, you'll gain a competitive edge in fraud detection across industry boundaries. From insurance fraud and credit card fraud to healthcare fraud and tax evasion, our techniques are applicable to various domains. Say goodbye to revenue leaks and hello to increased recovery, tighter security, and peace of mind.

Did you know that a typical organization loses about 5% of its revenue to fraud each year? It's time to put a stop to this alarming statistic. Our guidebook equips you with the knowledge and tools necessary to examine fraud patterns, gather and preprocess data, build models, and implement post-implementation strategies. You'll have a highly practical framework at your fingertips, putting an end to fraud before it spirals out of control.

Act now to prevent future fraud occurrences and take control of your organization's financial security! Click here to explore the world of fraud analytics and transform your approach to fraud detection.

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