Profile of an improper (corrupt) payment

If we were somehow able to see all of history’s bribes, kickbacks, conflicts of interests, duplicative payments, fake vendors, restricted entities and anomalous transactions from multiple companies and/or multiple regulatory enforcement actions, what would be some of the common threads? What common keywords do people use to describe an improper payment?

I posed these and other questions to some of the data scientists on my team who worked on a research project with the Anheuser-Busch InBev Foundation and the MIT researchers at Integrity Distributed (InDi), a nonprofit anti-fraud and anti-corruption think tank. In this research, we looked at the predictive modeling improvements when companies collaborate to fight corruption — without having to share the underlying data. Within that model, we can also analyze what attributes (or variables) were driving that model in hopes of unlocking the profile of an improper or corrupt payment.

Here were a few of my other queries for the research team:

• What’s the most common general ledger account an improper expense is booked to?
• What anti-fraud test is most common among the population of these known fraudulent transactions?
• Are manual payments the norm, or are they outliers? Are there occasions where manual payments are being abused for personal benefits?

Knowing whether a transaction is benign or indeed a high-risk or improper payment is an important distinction that often challenges prosecutors, lawyers and anti-fraud professionals like us. It takes time to gather evidence, review source documentation, run selected anti-fraud algorithms and analytics, and interview suspected individuals. If we could, with statistical confidence, quickly identify and remediate improper payments — perhaps even before they get paid out to a third party — we could not only save our organizations tremendous amounts of time and money, but also keep our organizations out of trouble with regulators.

Indeed, now more than ever, organizations using data analytics and collaborative initiatives such as InDi have an incentive to quickly and proactively identify and substantiate risks to determine if self-disclosure is necessary. That’s because, in February, the U.S. Department of Justice announced a new standard of voluntary self-disclosure. Under the new standard, one of the factors of leniency hinges on a company’s voluntary disclosure of misconduct to the U.S. Attorney’s Office “within a reasonably prompt time after the company becoming aware of the misconduct, with the burden being on the company to demonstrate timeliness,” potentially resulting in non-prosecution agreements and reduced fines. (See “United States Attorneys’ Offices Voluntary Self-Disclosure Policy,” justice.gov press release.)