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Applications of Artificial Intelligence and Machine Learning-based Supervisory Technology in Financial Markets Surveillance: A Review of Literature

Author

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  • Saurabh Maheshwari
  • Niti Nandini Chatnani

Abstract

Supervisory Technology (SupTech) combines artificial intelligence (AI) with machine learning (ML) for the specific purpose of supervision of the financial markets. SupTech offers transformative solutions for the regulation, monitoring and surveillance of financial markets. This article presents a literature review examining the role of SupTech in enhancing market surveillance. Using the PSALSAR framework, we conduct a Systematic Literature Review (SLR) of 55 articles published between 1999 and 2022. The review highlights the potential of SupTech to navigate the complexities of the financial landscape, and its versatile applications for financial supervisors and regulators, stock exchanges, intermediaries, and investors. Our findings indicate that SupTech models can effectively detect instances of market manipulations, insider trading, and fraud. These models also showcase exceptional potential to forecast crises in markets, economies, corporations, and in the banking sector. They are useful in empowering policymakers and regulators to make informed decisions. The study provides a strong foundation for future research and policy development to explore and deploy the potential of SupTech, paving the way for greater transparency, efficiency, and resilience in financial markets.

Suggested Citation

  • Saurabh Maheshwari & Niti Nandini Chatnani, 2025. "Applications of Artificial Intelligence and Machine Learning-based Supervisory Technology in Financial Markets Surveillance: A Review of Literature," FIIB Business Review, , vol. 14(5), pages 586-604, October.
    • Handle: RePEc:sae:fbbsrw:v:14:y:2025:i:5:p:586-604
    DOI: 10.1177/23197145231189990
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    1. Butaru, Florentin & Chen, Qingqing & Clark, Brian & Das, Sanmay & Lo, Andrew W. & Siddique, Akhtar, 2016. "Risk and risk management in the credit card industry," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 218-239.
    2. Atsalakis, George S. & Atsalaki, Ioanna G. & Pasiouras, Fotios & Zopounidis, Constantin, 2019. "Bitcoin price forecasting with neuro-fuzzy techniques," European Journal of Operational Research, Elsevier, vol. 276(2), pages 770-780.
    3. Katrin Assenmacher-Wesche & M. Hashem Pesaran, 2008. "Forecasting the Swiss Economy Using Vecx* Models: an Exercise in Forecast Combination Across Models and Observation Windows," National Institute Economic Review, National Institute of Economic and Social Research, vol. 203(1), pages 91-108, January.
    4. Błażej Prusak, 2018. "Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries," IJFS, MDPI, vol. 6(3), pages 1-28, June.
    5. Raminta Pranckutė, 2021. "Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World," Publications, MDPI, vol. 9(1), pages 1-59, March.
    6. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740, December.
    7. J. M. Calabuig & H. Falciani & E. A. S'anchez-P'erez, 2019. "Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets," Papers 1907.05697, arXiv.org, revised Mar 2020.
    8. Hogun Chong & Mary Zey & David A. Bessler, 2010. "On corporate structure, strategy, and performance: a study with directed acyclic graphs and PC algorithm," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 31(1), pages 47-62.
    9. Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Machine Learning Methods for Demand Estimation," American Economic Review, American Economic Association, vol. 105(5), pages 481-485, May.
    10. Rotimi Boluwatife Abidoye & Albert P.C. Chan & Funmilayo Adenike Abidoye & Olalekan Shamsideen Oshodi, 2019. "Predicting property price index using artificial intelligence techniques," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 12(6), pages 1072-1092, June.
    11. Foley, Sean & Kwan, Amy & McInish, Thomas H. & Philip, Richard, 2016. "Director discretion and insider trading profitability," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 28-43.
    12. repec:eme:ijhma0:ijhma-11-2018-0095 is not listed on IDEAS
    13. Climent, Francisco & Momparler, Alexandre & Carmona, Pedro, 2019. "Anticipating bank distress in the Eurozone: An Extreme Gradient Boosting approach," Journal of Business Research, Elsevier, vol. 101(C), pages 885-896.
    14. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    15. Fong, Tom Pak Wing & Wu, Shui Tang, 2020. "Predictability in sovereign bond returns using technical trading rules: Do developed and emerging markets differ?," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    16. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
    17. Mark Cecchini & Haldun Aytug & Gary J. Koehler & Praveen Pathak, 2010. "Detecting Management Fraud in Public Companies," Management Science, INFORMS, vol. 56(7), pages 1146-1160, July.
    18. Cumming, Douglas & Dannhauser, Robert & Johan, Sofia, 2015. "Financial market misconduct and agency conflicts: A synthesis and future directions," Journal of Corporate Finance, Elsevier, vol. 34(C), pages 150-168.
    19. Carole Comerton-Forde & James Rydge, 2006. "Market Integrity and Surveillance Effort," Journal of Financial Services Research, Springer;Western Finance Association, vol. 29(2), pages 149-172, April.
    20. Jaehyung An & Alexey Mikhaylov & Nikita Moiseev, 2019. "Oil Price Predictors: Machine Learning Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 9(5), pages 1-6.
    21. Dapeng Cui & David Curry, 2005. "Prediction in Marketing Using the Support Vector Machine," Marketing Science, INFORMS, vol. 24(4), pages 595-615, January.
    22. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
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