Data-Driven Risk Management: Detect Fraud Before the Tip-Off
In this article, Gregory Bates, Counsel for Miller & Chevalier, and his co-author Parth Chanda, Founder and CEO of Lextegrity, discuss a solution to detecting occupational fraud schemes earlier and at a much greater rate - with data-driven risk management. An estimated 43 percent of occupation fraud is detected through tips or whistleblower reports, but those reports often come too late. "Data-driven risk management describes the prioritization of data insights (from inside and outside your organization) to assess and manage risk holistically," the authors wrote. "Layering" data sets from various sources can make it easier to detect compliance risk at a much earlier stage – or even prevent compliance violations from occurring. According to the authors, a data-driven approach matters because (1) it satisfies regulatory requirements, (2) fraud and other financial crimes can be detected faster, (3) an organization's teams can focus their efforts on high value activity, (4) an organization's board can make better decisions, and (5) an organization can accelerate digital transformation. Embracing data-driven risk management will help an organization implement the updated Department of Justice (DOJ) guidance relating to the use of data within their compliance program. "A data-driven approach can provide opportunities for better-targeted efforts across the risk and compliance enterprise, allowing experts to focus their time more effectively," the authors wrote, adding that organizations should "begin by understanding the data [they] need to analyze and gain access to it. [Organizations should] start with data vendor payments and employee expenses."