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Data-driven risk management: Detect fraud before the tip-off

Parth Chanda (pchanda@lextegrity.com) is Founder and CEO of Lextegrity, based in New York City. Greg Bates (gbates@milchev.com) is Counsel for Miller & Chevalier in Washington, DC.

An estimated 43% of occupational fraud is detected by way of a tip—or whistleblower report—according to the latest edition of the Association of Certified Fraud Examiners’ Report to the Nations.[1] That’s a huge proportion, and one that emphasizes the value of reporting channels as a method for detecting occupational fraud schemes.

But tips often come too late. What’s more, not every fraud scheme is detected internally—if at all.

This raises obvious and tantalizing questions: What if it was possible to detect occupational fraud schemes earlier, and what if it was also possible to detect those schemes at a much greater rate?

For a growing number of forward-thinking enterprises, this seemingly unreachable goal is becoming a reality. For them, the solution lies within data-driven risk management.

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