Skip to main content
SpringerLink
Log in

Toward automated regulatory compliance

  • Original Research
  • Published:
CSI Transactions on ICT Aims and scope Submit manuscript

Abstract

Businesses are getting increasingly regulated. Regulatory compliance is a board level concern and one of the top-3 CEO level concerns across business verticals. Failure to comply leads not only to heavy fines but reputational risk too. Current practice of regulatory compliance is document-centric, and therefore, heavily reliant on human experts. Given the large size of modern enterprises, their multi-geography operation, increasing dynamics, and frequent changes in regulations, the current practice of regulatory compliance is found wanting on correctness, responsiveness, and scale. Introduction of appropriate technology seems necessary to overcome these challenges. In this paper, we present an AI-aided model-driven automated approach to regulatory compliance and supporting technology infrastructure. We describe how the approach has fared in real-world industry-scale context and outline future work.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. http://www.smbceo.com/2020/02/08/what-is-the-role-of-the-ceo-in-regulatory-compliance/

  2. https://www.bankingexchange.com/bsa-aml/item/8202-cost-of-compliance-expected-to-hit-181bn

  3. https://thefinanser.com/2017/01/bank-regulations-change-every-12-minutes.html/

  4. https://www.ibm.com/learn/cloud/grc

  5. https://www.omg.org/spec/SBVR

  6. http://www.inf.ed.ac.uk/teaching/courses/pm/Note16.pdf.

  7. https://www.omg.org/spec/SBVR/1.3

  8. https://www.omg.org/spec/MOFM2T/

  9. https://www.omg.org/spec/QVT/

  10. https://www.drools.org/

  11. https://www.esma.europa.eu/policy-rules/mifid-ii-and-mifir

References

  1. Sergot MJ, Sadri F, Kowalski RA, Kriwaczek F, Hammond P, Cory HT (1986) The British nationality act as a logic program. Commun ACM 29(5):370–386

    Article  Google Scholar 

  2. Antoniou G, Dimaresis N, Governatori G (2007). A system for modal and deontic defeasible reasoning. In AI 2007: Advances in artificial intelligence, 20th Australian joint conference on artificial intelligence, Gold Coast, Australia, December 2–6, 2007, proceedings

  3. Kerrigan S, Law KH (2003). Logic-based regulation compliance-assistance. In Proceedings of the 9th international conference on artificial intelligence and law, ICAIL 2003, Edinburgh, Scotland, UK, June 24–28, 2003. pp. 126–135

  4. Zeni N, Kiyavitskaya N, Mich NL, Cordy JR, Mylopoulos J (2015) GaiusT: supporting the extraction of rights and obligations for regulatory compliance. Requir Eng 20(1):1–22

    Article  Google Scholar 

  5. Sagar S, Kholkar D, Kulkarni V (2016) Informed active learning to aid domain experts in modeling compliance. In: 2016 IEEE 20th international enterprise distributed object computing conference (EDOC). IEEE

  6. Sagar S, Kholkar D, Kulkarni V (2016) Comparison and synergy between fact-orientation and relation extraction for domain model generation in regulatory compliance. In: International conference on conceptual modeling. Springer, Cham

  7. Suman R, et al. (2018) A case study on modeling and validating financial regulations using (semi-) automated compliance framework. In: IFIP Working conference on the practice of enterprise modeling. Springer, Cham

  8. Sunkle S, Kholkar D, Kulkarni V (2015). Model-driven regulatory compliance: a case study of “Know Your Customer” regulations. In 18th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MoDELS 2015, Ottawa, ON, Canada, September 30 - October 2, 2015. 436–445

  9. Deepali K, Mulpuru D, Kulkarni V (2018) Balancing model usability and verifiability with SBVR and answer set programming. In: MODELS Workshops, pp. 570–573

  10. Nijssen S (2007) SBVR: Semantics for business

  11. Leone N, Pfeifer G, Faber W, Eiter T, Gottlob G, Perri S, Scarcello F (2006) The DLV system for knowledge representation and reasoning. ACM Trans Comput Log 7(3):499–562

    Article  MathSciNet  Google Scholar 

  12. Lifschitz V (2008) What is answer set programming? In: Proceedings of the twentythird AAAI conference on artificial intelligence, AAAI 2008, Chicago, Illinois, USA, July pp. 13–17

  13. Dimaresis N (2007) A system for modal and deontic defeasible reasoning. Int J Cooperative Inf Syst 14(2–3):181–216

    Google Scholar 

  14. Yeddula RR, Das P, Reddy S (2015) A model-driven approach to enterprise data migration. In: Zdravkovic J, Kirikova M, Johannesson P (eds). Conference on advanced information systems engineering (CAISE), Stockholm, Sweden, volume 9097 of Lecture Notes in Computer Science, pages 230–243. Springer.

  15. Ronen F, Sanger J (2007) The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press, Cambridge

    Google Scholar 

  16. Vinay K, Barat S, Clark T (2019) Towards adaptive enterprises using digital twins. In: 2019 Winter simulation conference (WSC), pp. 60–74. IEEE

  17. Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255–260

    Article  MathSciNet  Google Scholar 

  18. Sutton RS, Barto AG (2018) Reinforcement learning: an introduction. MIT press, Cambridge

    MATH  Google Scholar 

  19. Schmidt DC (2006) Model-driven engineering. Computer-IEEE Comput Soc 39(2):25

    Article  Google Scholar 

  20. Kulkarni V, Reddy S, Rajbhoj A (2010) Scaling up model driven engineering–experience and lessons learnt. In: International conference on model driven engineering languages and systems, pp. 331–345. Springer, Berlin, Heidelberg

Download references

Author information

Authors and Affiliations

  1. Tata Consultancy Services, Pune, India

    Vinay Kulkarni, Sagar Sunkle, Deepali Kholkar, Suman Roychoudhury, Rahul Kumar & M. Raghunandan

Authors
  1. Vinay Kulkarni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sagar Sunkle
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Deepali Kholkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Suman Roychoudhury
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rahul Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M. Raghunandan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vinay Kulkarni.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kulkarni, V., Sunkle, S., Kholkar, D. et al. Toward automated regulatory compliance. CSIT 9, 95–104 (2021). https://doi.org/10.1007/s40012-021-00329-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40012-021-00329-4

Keywords

Search

Navigation