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.
What is data-driven risk management?
Data-driven risk management describes the prioritization of data insights (from inside and outside your organization) to assess and manage risk holistically.
The use of data to support business strategy and decision-making is nothing new. Business functions like marketing and sales have used data insights to assess, predict, and capitalize on revenue growth opportunities for decades. However, the traditional approach to using data within a risk management context has typically been siloed, making it almost impossible to see potentially useful data within a meaningful broader context.
When it comes to detecting fraud, hotline reports have long been a key detection tool. Yet frauds detected in this way mean any related data tends to be historic (and often well after a financial crime has been committed), lacking in quantitative detail, and only available if a report is submitted at all.
Truly effective data-driven risk management makes use of data sets from multiple sources to build a more detailed picture. Layering these various data sets on top of each other provides context, enabling compliance, risk, and audit teams to make more informed, data-led decisions. It can provide analytical data on an ongoing basis to illustrate whether existing controls are working instead of simply flagging instances where they have failed. It also makes it possible to detect financial crimes at a much earlier stage or even prevent them from occurring.
