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: