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Big Data Fraud

Big Data Fraud

Finding the needle in the haystack.

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National Forensic Leader

KPMG in Canada

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In the not-so-distant past, organizations faced a major hurdle in leveraging analytics to fight fraud: technical skills. People who could manipulate large data sets were scarce. Recently, riding the latest "Big Data" wave, analytical tools have exploded in availability, and improved dramatically in terms of affordability and usability. The ability to analyze data is now widespread.

Yet wide-scale deployments of tools such as Alteryx, Tableau and Qlikview eventually revealed another issue: Generic, one-size fits all analytics were very good at finding false positives, and often terrible at fighting fraud. The missing ingredient in most analyses: context.

While a pro baseball general manager can use analytics to evaluate a player, he should also look at the context in which that data was produced. This will inform the decision of what metrics (science) mean the most, and allow him to make adjustments based on his judgment (art). If two players have comparable home runs in college, but one of them played in higher altitude where balls travel further, should this be factored into that player's evaluation?

This article provides examples of how analytics are used to fight fraud, and how context is a crucial determinant in the success of the analytics.

Read the full PDF to learn more.

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