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1 RAIL 175 (2018)
Enhancing Regulatory Compliance by Using Artificial Intelligence Text Mining to Identify Penalty Clauses in Legislation

handle is hein.journals/rail1 and id is 182 raw text is: Enhancing Regulatory Compliance by Using Artificial Intelligence Text Mining to Identify Penalty Clauses in Legislation Nachshon Goltz and Michael Mayo* This article provides the theoretical basis of machine learning for text clas- sification and presents a two-stage experiment of(1) training multiple models and selecting the best one, and (2) employing a sliding window detection in order to identify penalty clauses in regulation. As regulatory compliance (or compliance governance) becomes ever more challenging, attempts to engage IT solutions and espe- cially artificial intelligence (Al) are on the rise. This article sug- gests that regulatory compliance can be enhanced by employing an Al model trained to identify penalty clauses in the regulations. The article provides the theoretical basis of machine learning for text classification and presents a two-stage experiment of (1) training multiple models and selecting the best one, and (2) employing a sliding window detection in order to identify penalty clauses in regulation. Results benchmarked using an algorithm-based penal- ties API suggests further development is needed. Introduction Challenges in regulatory compliance are closely associated with the increased business opportunities resulting from globalization and Information Communication Technology. There is a drastic increase in the regulatory requirements with which businesses must comply, not only in sheer number but also in complexity, confronting busi- nesses with the need to adapt to a complex and evolving regulatory environment.2 In such an environment, organizations are finding compliance with the numerous regulations to be very expensive, with an estimated $1 trillion spent worldwide on regulatory compliance. Robotics, Artificial Intelligence & Law I May-June 2018, Vol. 1, No. 3, pp. 175-191. @ 2018 Full Court Press. All rights reserved. ISSN 2575-5633 (print)/ISSN 2575-5617 (online)

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