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Machine learning: A revolution in risk management and compliance?

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Abstract

Financial institutions (FIs) are increasingly looking to deploy machine learning approaches to manage and mine regulatory reporting data and unstructured information. This article aims to introduce the machine learning field and discusses several "regtech" application cases within FIs, based on discussions with the sector and with technology ventures: credit risk modeling, detection of credit card fraud and money laundering, and surveillance of conduct breaches. Two tentative conclusions emerge on the added value of applying machine learning in the financial services sector. First, the ability of machine learning methods to analyze very large amounts of data, while offering a high granularity and depth of predictive analysis, can significantly improve analytical capabilities across risk management and compliance areas, such as money laundering detection and credit risk modeling. Second, the application of machine learning approaches within the financial services sector is highly context-dependent. Data quality and availability can be an issue; more importantly, the predictive performance and granularity of analysis of several approaches can come at the cost of increased model complexity and a lack of explanatory insight. This is an issue particularly where analytics are applied in a regulatory context, and a supervisor or compliance team will want to audit and understand the applied model.

Suggested Citation

  • van Liebergen, Bart, 2017. "Machine learning: A revolution in risk management and compliance?," Journal of Financial Transformation, Capco Institute, vol. 45, pages 60-67.
    • Handle: RePEc:ris:jofitr:1592
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    Citations

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    Cited by:

    1. John R. J. Thompson & Longlong Feng & R. Mark Reesor & Chuck Grace, 2021. "Know Your Clients’ Behaviours: A Cluster Analysis of Financial Transactions," JRFM, MDPI, vol. 14(2), pages 1-29, January.
    2. Adam Bouland & Wim van Dam & Hamed Joorati & Iordanis Kerenidis & Anupam Prakash, 2020. "Prospects and challenges of quantum finance," Papers 2011.06492, arXiv.org.
    3. Stijn Claessens & Jon Frost & Grant Turner & Feng Zhu, 2018. "Fintech credit markets around the world: size, drivers and policy issues," BIS Quarterly Review, Bank for International Settlements, September.
    4. Jon Frost & Leonardo Gambacorta & Yi Huang & Hyun Song Shin & Pablo Zbinden, 2019. "BigTech and the changing structure of financial intermediation," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 34(100), pages 761-799.
    5. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    6. Cosma, Simona & Rimo, Giuseppe & Torluccio, Giuseppe, 2023. "Knowledge mapping of model risk in banking," International Review of Financial Analysis, Elsevier, vol. 89(C).
    7. Flavio Bazzana & Marco Bee & Ahmed Almustfa Hussin Adam Khatir, 2024. "Machine learning techniques for default prediction: an application to small Italian companies," Risk Management, Palgrave Macmillan, vol. 26(1), pages 1-23, February.
    8. Wall, Larry D., 2018. "Some financial regulatory implications of artificial intelligence," Journal of Economics and Business, Elsevier, vol. 100(C), pages 55-63.
    9. Evgeny Moiseev & Denis Zagorodnev & Alexander Berezinskiy & Roman Tikhonov, 2022. "A Method for Assessing the IT Component of Model Risk and the Economic Capital to Cover It," Russian Journal of Money and Finance, Bank of Russia, vol. 81(3), pages 107-127, September.
    10. Michael Becker & Rüdiger Buchkremer, 2018. "Ranking of current information technologies by risk and regulatory compliance officers at financial institutions – a German perspective," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 10(1), pages 007-026, June.
    11. Sebastian Doerr & Jon Frost & Leonardo Gambacorta & Vatsala Shreeti, 2023. "Big techs in finance," BIS Working Papers 1129, Bank for International Settlements.
    12. Małgorzata Zaleska & Edyta Cegielska & Emil Ślązak, 2020. "Employment in the banking sector in Poland – determinants and perception," Bank i Kredyt, Narodowy Bank Polski, vol. 51(6), pages 661-686.
    13. Paolo Vanini & Sebastiano Rossi & Ermin Zvizdic & Thomas Domenig, 2023. "Online payment fraud: from anomaly detection to risk management," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    14. Anil Savio Kavuri & Alistair Milne, 2019. "FinTech and the future of financial services: What are the research gaps?," CAMA Working Papers 2019-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    15. Michael Becker & Kevin Merz & Rüdiger Buchkremer, 2020. "RegTech—the application of modern information technology in regulatory affairs: areas of interest in research and practice," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 161-167, October.

    More about this item

    Keywords

    Machine learning; compliance; risk management; regtech; fintech; econometric modeling;
    All these keywords.

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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