Shruti Shah (sshah@coalitionforintegrity.org) is President and CEO of the Coalition for Integrity in Washington, DC. Jonathan J. Rusch (jonathan.j.rusch@gmail.com) is Adjunct Professor at Georgetown University and American University Washington College of Law and Principal at DTG Risk & Compliance LLC, based in Washington, DC.
As they work to maintain the effectiveness of their anti-corruption risk and compliance programs, companies must be increasingly attentive to how well they make use of the acquired data relevant to those programs. The most recent edition of the U.S. Department of Justice’s Evaluation of Corporate Compliance Programs states that prosecutors should inquire into whether compliance and control personnel “have sufficient direct or indirect access to relevant sources of data to allow for timely and effective monitoring and/or testing of policies, controls, and transactions,” and whether “any impediments exist that limit access to relevant sources of data.”[1]
Companies, however, are increasingly awash in such data from a multiplicity of sources: accounts payable, spend data, and third-party supplier data, to name just a few. Many companies make use of rule-based programming, in which human programmers write rules that enable the company to search and find data indicative of corruption risk. But some companies are increasingly curious about whether they should use a particular field of artificial intelligence, machine learning, in which computer systems “learn” on their own from data and do not depend on human-written rules.
To assist companies in this process, the Coalition for Integrity recently issued a guidance document, Using Machine Learning for Anti-Corruption Risk and Compliance.[2] The guidance states that it is intended “to provide companies in multiple sectors with guidance on whether and how they should consider developing or acquiring anti-corruption machine learning.” In particular, the guidance has four stated objectives:
