Fraud and Fraud Detection: A Data Analytics Approach, + Website

THE MUST-HAVE GUIDE TO USING BIG DATA AND AUTOMATION TO DETECT FRAUD FASTER

Sooner or later, every fraudster makes a mistake. Fraud and Fraud Detection: A Data Analytics Approach will show you how to automate statistical and data analytical tests to catch those mistakes, identify criminals, and eliminate the fraud.

With big data analytics, you can find and stop fraudulent transactions much more efficiently than ever before. Detecting a few tiny abnormalities among thousands or millions of legitimate transactions has become a simple matter thanks to big data analytics, and Fraud and Fraud Detection explains how you can use these new techniques to mitigate losses and stop valuable resources from seeping through the cracks.

Fraud and Fraud Detection will help you reach the next phase in fraud mitigation so you can keep up with the latest challenges. You'll learn how to automatically apply key tests, including variability from the center, Benford's Law, the number duplication test, the relevant size factor test, and many more, to all the major types of fraud. Starting with how to identify important data sets and ending with implementing a well-designed, automated fraud detection plan, this book covers every step of the process.

Fraud and Fraud Detection explains what you need to understand about big data and provides a step-by-step guide to implementing the most important tools and techniques—including templates, downloadable automation scripts, and access to the CaseWare IDEA software. This approach has been ready to go for years, and it's time you took advantage of big data to uncover needless losses.