Technology is rapidly disrupting every industry and institution around the globe. Yet, corporate compliance has remained relatively unaffected by technological change when compared to other industries. If firms continue to lag behind in their compliance efforts, their risk exposure to the potentially lethal sanctions associated with major compliance failures will continue to increase with time. This is particularly true in the context of the Foreign Corrupt Practices Act (“FCPA”). Generally, the FCPA is a regulatory statute that forbids bribery and false accounting for domestic firms doing business abroad. And, in the past decade the DOJ and SEC have begun aggressively enforcing the FCPA. Firms should begin using technology to develop more robust and cost-efficient compliance programs in order to insulate themselves from the FCPA’s harsh penalties.

This paper provides an algorithm that allows firms to evaluate and improve their compliance programs in accordance with several published sources of guidance. Compliance scholars have made clear that it is critical for firms to maintain strong corporate compliance programs and have suggested different models and frameworks for internal evaluation and auditing. However, those suggestions fail to consider how technology may be used to improve the cost-efficiency of corporate compliance & ethics programs. This paper takes an informatics based approach to evaluating and improving firm compliance by focusing on the most important compliance functions according to the DOJ, courts, and other Government actors. Indeed, firms may drastically improve the cost-efficiency of their compliance efforts by adopting the analytical framework proposed in this paper.


Haney, Brian Seamus, Calculating Corporate Compliance & The Foreign Corrupt Practices Act (April 1, 2018). HANEY, Brian S.. Calculating Corporate Compliance & The Foreign Corrupt Practices Act. Pittsburgh Journal of Technology Law and Policy, [S.l.], v. 19, n. 1, Jan. 2019. ISSN 2164-800X. Available at SSRN: https://ssrn.com/abstract=3261443 or http://dx.doi.org/10.2139/ssrn.3261443

Brian Seamus Haney

Brian’s practice focuses on technology startups and legal automation. He has experience working with startup organizations developing natural language processing algorithms and regularly writes on the intersections of law & artificial intelligence. Brian’s writing emphasizes an informatics based approach toward legal automation and provides extensive analysis of regulatory issues related to deep reinforcement learning systems. Brian is an attorney with the law firm of Eckert, Seamans, Cherin & Mellott in Pittsburgh, Pennsylvania.


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