Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques

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Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection (Wiley and SAS Business Series) 1st Edition

Uncover fraudulent activities sooner to minimize losses and halt potential cascading harm

The guidebook titled Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative manual for establishing a robust fraud detection analytics solution. Recognizing fraud early is crucial for minimizing damage, but it necessitates specialized techniques distinct from those applied in the later stages of fraud detection. This invaluable guide delves into both the theoretical underpinnings and technical intricacies of these techniques, offering expert insights to streamline their implementation. The coverage spans data gathering, preprocessing, model building, and post-implementation, providing comprehensive guidance on various learning techniques and the types of data utilized in each. The effectiveness of these techniques extends across industry boundaries, encompassing applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, providing a highly practical framework for fraud prevention.

Studies estimate that an average organization loses approximately 5% of its revenue to fraud annually. This book contends that more effective fraud detection is attainable and outlines the analytical techniques organizations must implement to plug the revenue leak.

Key highlights include:

1. Analyzing fraud patterns in historical data
2. Leveraging labeled, unlabeled, and networked data
3. Identifying fraud before it triggers cascading damage
4. Minimizing losses, enhancing recovery, and fortifying security

Allowing fraud to persist has far-reaching consequences. It expands exponentially, sending waves of damage throughout the organization and evolving into a complex challenge to track, stop, and reverse. Effective fraud prevention hinges on early and efficient detection, a capability facilitated by the techniques expounded in this guide. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques empowers you to thwart fraud in its early stages and eliminate opportunities for its recurrence.