by Shruti Shah and Jonathan J. Rusch
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 data they acquire that are relevant to those programs. The most recent edition of the U.S. Department of Justice’s “Evaluation of Corporate Compliance Programs” document 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, 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 for 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.