ПОТЕНЦІАЛ ВИКОРИСТАННЯ ТЕХНОЛОГІЙ ШТУЧНОГО ІНТЕЛЕКТУ ДЛЯ ЗДІЙСНЕННЯ ПРУДЕНЦІЙНОГО НАГЛЯДУ ЗА КРЕДИТНИМИ УСТАНОВАМИ
DOI:
https://doi.org/10.32782/ecovis/2026-3-20Ключові слова:
штучний інтелект, пруденційний нагляд, SupTech, машинне навчання, кредитні установи, ризик-менеджмент, стрес-тестуванняАнотація
Стаття присвячена дослідженню потенціалу технологій штучного інтелекту (ШІ) для здійснення пруденційного нагляду за діяльністю кредитних установ. Актуальність теми зумовлена тим, що традиційні наглядові методи, які спираються на аналіз ретроспективних даних і виїзні перевірки, поступово втрачають ефективність в умовах зростання обсягу та динамічності фінансових ризиків, а глобальні банківські кризи засвідчили обмеженість стандартного наглядового інструментарію. На основі узагальнення світового досвіду визначено перспективні напрями розвитку у середньостроковій перспективі. Автори роблять висновок, що ефективна реалізація потенціалу ШІ в пруденційному нагляді потребує комплексного підходу – формування адекватної нормативно-правової бази, інвестицій у цифрову інфраструктуру, розвитку цифрових компетенцій персоналу наглядових органів і забезпечення прозорого управління ризиками.
Посилання
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Beerman K., Prenio J., Zamil R. Suptech tools for prudential supervision and their use during the pandemic. FSI Insights on policy implementation. No. 37. Basel: Bank for International Settlements, December 2021. URL: https://www.bis.org/fsi/publ/insights37.html
Prenio J. Peering through the hype — assessing suptech tools' transition from experimentation to supervision. FSI Insights on policy implementation. No. 58. Basel: Bank for International Settlements, June 2024. URL: https://www.bis.org/fsi/publ/insights58.pdf
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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