As 2022 showed us with recent DOJ settlements, companies who invest in monitoring and anti-fraud/anti-corruption technology (whether in-house or commercial) will be looked upon more favorably than those who don’t. If your organization is a technology-driven company, yet you are not applying technology in your compliance monitoring efforts, then that lack of congruency could come back to bite you.
But fear not! In many cases, organizations can pull in data science professionals from other departments on a loan staff (or part time) basis to look at key processes and identify areas for improvement. Compliance professionals may also look to outside consultants to fill temporary gaps, but that can be expensive in the long-run. The compliance department of the future is indeed, a multidisciplinary team – perhaps led by legal professionals, but also includes data science, investigative and information technology professionals as well.
Anheuser-Busch InBev was one of the first among global organizations to bring third-party transaction monitoring in house over five years ago. However, that endeavor was quite expensive and cost several millions of dollars to implement. Now, with advances in technology and automation, companies can bring in leading compliance monitoring technology for a fraction of the costs – e.g., less than the cost of one full time senior compliance professional.
Similar to what litigation departments did over a decade ago when bringing eDiscovery technology in house (because outsourcing eDiscovery became quite expensive as data sets got larger), leading compliance organizations will bring compliance monitoring technology in-house, cost effectively, with measurable results and effectiveness. In the words of the DOJ in their June 2020 Guidance, “Does the company engage in risk management of third parties throughout the lifespan of the relationship, or primarily during the onboarding process?”
In December of 2023, a not-for-profit foundation out or MIT named Integrity Distributed released its first anti-corruption research report demonstrating that when companies collaborate around third-party payments and the respective attributes of high-risk transactions, the results indicate that the predictive value of identifying a potentially improper payment is 25% greater when compared to results where each company’s model is performed in isolation.
Indeed, 2023 will be a break-though year as companies begin collaborating on anti-corruption innovation. Using split-learning techniques out of MIT, for example, companies can safely collaborate around key attributes of a high risk third party payment or transaction, without having to share the underlying data. Download the Integrity Distributed white paper here to learn more about the data sharing consortium.