Research Commons
      • Browse 
        • Communities & Collections
        • Titles
        • Authors
        • By Issue Date
        • Subjects
        • Types
        • Series
      • Help 
        • About
        • Collection Policy
        • OA Mandate Guidelines
        • Guidelines FAQ
        • Contact Us
      • My Account 
        • Sign In
        • Register
      View Item 
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
      • View Item
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Enhancing regulatory compliance by using artificial intelligence text mining to identify penalty clauses in legislation

      Goltz, Nachshon (Sean); Mayo, Michael
      Thumbnail
      Files
      SSRN-id2977570.pdf
      Accepted version, 412.7Kb
      Link
       papers.ssrn.com
      Citation
      Export citation
      Goltz, N., & Mayo, M. (2017). Enhancing regulatory compliance by using artificial intelligence text mining to identify penalty clauses in legislation. Presented at the MIREL 2017 - Workshop on ‘Mining and REasoning with Legal texts’, held in conjunction with the 16th International Conference on Artificial Intelligence and Law, King’s College, London, UK.
      Permanent Research Commons link: https://hdl.handle.net/10289/11940
      Abstract
      As regulatory compliance (or compliance governance) becomes ever more challenging, attempts to engage IT solutions and especially artificial intelligence (AI) are on the rise. This paper suggest that regulatory compliance can be enhanced by employing an AI model trained to identify penalty clauses in the regulations. The paper 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 penalties API suggests further development is needed.
      Date
      2017
      Type
      Conference Contribution
      Rights
      © 2017 copyright with the authors.
      Collections
      • Computing and Mathematical Sciences Papers [1452]
      Show full item record  

      Usage

      Downloads, last 12 months
      73
       
       

      Usage Statistics

      For this itemFor all of Research Commons

      The University of Waikato - Te Whare Wānanga o WaikatoFeedback and RequestsCopyright and Legal Statement