THE POTENTIAL OF USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR PRUDENTIAL SUPERVISION OF CREDIT INSTITUTIONS
DOI:
https://doi.org/10.32782/ecovis/2026-3-20Keywords:
artificial intelligence, prudential supervision, SupTech, machine learning, credit institutions, risk management, stress testingAbstract
The article is devoted to the study of the potential of artificial intelligence (AI) technologies for prudential supervision of the activities of credit institutions. The relevance of the topic is determined by the fact that traditional supervisory methods, based on the analysis of retrospective data and on-site inspections, are gradually losing their effectiveness under conditions of growing volume and dynamism of financial risks, while global banking crises have demonstrated the limitations of standard supervisory tools. In this context, supervisory technologies (SupTech) are considered as a tool for the transition from a reactive to a preventive model of prudential supervision. The article distinguishes between the concepts of SupTech (supervisory technologies applied by regulators) and RegTech (technologies used by credit institutions to comply with regulatory requirements), and also substantiates the specifics of AI application in supervisory activities as distinct from the banking sector. The key challenges of implementing AI in supervisory practice are analyzed: the problem of algorithm explainability, the complexity of independent model validation, the risks of systematic biases, dependence on the quality of input data, data confidentiality issues, staffing limitations, and the fragmentation of regulatory and legal frameworks. Based on a generalization of world experience, the most promising areas of development in the medium term are identified: the use of generative and agentic AI in supervisory processes; the development of tools for overseeing the AI systems of credit institutions themselves; the transition to real-time prudential supervision; and the strengthening of international coordination in the field of SupTech. The authors conclude that the effective realization of AI potential in prudential supervision requires a comprehensive approach – the formation of an adequate regulatory and legal framework, investment in digital infrastructure, the development of digital competencies among supervisory authority staff, and the provision of transparent risk management.
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Suss J., Treitel H. Predicting bank distress in the UK with machine learning. Staff Working Paper No. 831. London: Bank of England, October 4, 2019. URL: https://www.bankofengland.co.uk/working-paper/2019/predicting-bank-distress-in-the-uk-with-machine-learning
Bluwstein K., Buckmann M., Joseph A., Kang M., Kapadia S., Simsek O. Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach. Bank of England Staff Working Paper No. 848. London: Bank of England, 2020.
Deloitte Insights. Unleashing the power of machine learning models in banking through explainable artificial intelligence (XAI). Deloitte, May 2022. URL: https://www2.deloitte.com/us/en/insights/industry/financial-services/explainable-ai-in-banking.html
Wolters Kluwer. Banking on AI: Risk, readiness, and the next frontier. Wolters Kluwer, 2026. URL: https://www.wolterskluwer.com/en/expert-insights/banking-on-ai-risk-readiness-and-the-next-frontier
Financial Stability Board (FSB). Artificial intelligence and machine learning in financial services. Basel: FSB, November 2017. URL: https://www.fsb.org/uploads/P011117.pdf
Wang T., Zhao S., Zhu G., Zheng H. A machine learning-based early warning system for systemic banking crises. Applied Economics. 2021. Vol. 53, No. 26. P. 2974–2992. DOI: https://doi.org/10.1080/00036846.2020.1870657. URL: https://www.researchgate.net/publication/348562944
Financial Stability Board (FSB). The Financial Stability Implications of Artificial Intelligence. Basel: FSB, November 14, 2024. URL: https://www.fsb.org/uploads/P14112024.pdf
Reimann C. Predicting financial crises: an evaluation of machine learning algorithms and model explainability for early warning systems. Review of Evolutionary Political Economy. 2024. Vol. 5. P. 51–83. DOI: https://doi.org/10.1007/s43253-024-00114-4. URL: https://www.researchgate.net/publication/380010473
McCaul E. From data to decisions: AI and supervision. European Central Bank Banking Supervision. February 26, 2024. URL: https://www.bankingsupervision.europa.eu/press/interviews/date/2024/html/ssm.in240226~c6f7fc9251.en.html
Bank of England; Financial Conduct Authority. Artificial intelligence in UK financial services – 2024. London: Bank of England, 2024. URL: https://www.bankofengland.co.uk/report/2024/artificial-intelligence-in-uk-financial-services-2024
UK Parliament. AI in financial services: Responses to the Committee's Fifteenth report. April 2026. URL: https://publications.parliament.uk/pa/cm5901/cmselect/cmtreasy/1791/report.html
Monetary Authority of Singapore (MAS). MAS Partners Industry to Develop AI Risk Management Toolkit for the Financial Sector. Press release. Singapore: MAS, March 2026. URL: https://www.mas.gov.sg/news/media-releases/2026/mas-partners-industry-to-develop-ai-risk-management-toolkit-for-the-financial-sector
Monetary Authority of Singapore (MAS). MAS proposes guidelines for AI risk management: Consultation Paper on Guidelines on Artificial Intelligence Risk Management. Singapore: MAS, 13 November 2025.
Monetary Authority of Singapore (MAS). Principles to Promote Fairness, Ethics, Accountability and Transparency (FEAT) in the Use of Artificial Intelligence and Data Analytics in Singapore's Financial Sector. Singapore: MAS, 2018.
Government Accountability Office (GAO). Artificial Intelligence: Use and Oversight in Financial Services (GAO-25-107197). Washington: GAO, 2025. URL: https://files.gao.gov/reports/GAO-25-107197/index.html
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Copyright (c) 2026 Наталія Холявко, Ірина Мурашковська
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