Fraud examiner thought leaders are working to innovate anti-fraud processes. In this issue, column editor Vincent M. Walden, CFE, CPA, collaborates with Todd Marlin, CISSP, CCSP, CISM, of EY’s Forensic & Integrity Services practice. — ed.
New developments in forensic data analytics are breaking down traditional barriers to access, transparency and usability. You don’t need an advanced degree to use “data blending” software.
You're the regional compliance or internal audit manager of a global manufacturing company responsible for monitoring high-risk transactions and investigating suspicious or noncompliant activities. For years, you’ve relied on spreadsheets, database queries and random sample selections to scrutinize data and select areas for testing and due diligence. But you’re now so overwhelmed reacting to whistleblower hotline calls coming down from headquarters that you rarely have time to think about proactive anti-fraud data analytics procedures. Sometimes, you feel like you’re drinking from a firehose and wish you could be more proactive than reactive.
As a CFE, you know how to analyze data almost as well as you know how to interview people. This is particularly true with financial data such as payments, sales and commissions, and travel and entertainment, among other sources. You’ve even had your fair share of third-party due diligence and compliance reviews and the occasional email investigation where you found that smoking-gun document.
You also hear about digital transformation within your industry, but you wonder how it practically applies to you when you’re measuring risk management, compliance and business integrity. You’ve heard war stories of how difficult it is to extract, normalize and integrate data within your organization so you can properly analyze it. “There’s simply too much data out there,” you tell yourself. “And even if I could get it centralized, there’s no way to cost effectively synthesize that data into something meaningful where I could take action.”
You fear that any commercial data analytics (or business intelligence) solution is just a confining, rigid “black box” — where you don’t fathom the algorithms, the math is too complex and the results show too many false positives, such as potentially bogus invoices or vendors.
However, this model has changed.
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