IMPROVING THE EARLY WARNING SYSTEM FOR CREDIT AND MARKET RISKS BASED ON DIGITAL INDICATORS
DOI:
https://doi.org/10.60078/3060-4842-2026-vol3-iss4-pp108-117Abstract
The article examines approaches to improving the early warning system for credit and market risks based on digital indicators. Transactional, payment, financial, behavioural, and market indicators capable of identifying adverse changes before risk materialisation are analysed. A multi-level mechanism for classifying warning signals and linking them to bank management decisions is proposed. The study substantiates the use of dynamic thresholds and individual digital risk profiles.
Keywords:
credit risk market risk early warning system digital indicators commercial banks risk monitoring risk management digital profileReferences
Bank for International Settlements. (2024). Annual Economic Report 2024: Artificial Intelligence and the Economy – Implications for Central Banks. Basel: BIS. https://www.bis.org/publ/arpdf/ar2024e3.htm.
Beltman, J., Machado, M. R., & Osterrieder, J. R. (2025). Predicting retail customers’ distress in the finance industry: An early warning system approach. Journal of Retailing and Consumer Services, 82, 104101. https://doi.org/10.1016/j.jretconser.2024.104101.
Chang, V., Xu, Q. A., Akinloye, S. H., Benson, V., & Hall, K. (2024). Prediction of bank credit worthiness through credit risk analysis: An explainable machine learning study. Annals of Operations Research. https://doi.org/10.1007/s10479-024-06134-x.
Del Nero, L., & Giudici, P. (2025). Machine learning models to predict stock market spillovers. Finance Research Letters, 86(C), 108508. https://doi.org/10.1016/j.frl.2025.108508.
Financial Stability Board. (2024). The Financial Stability Implications of Artificial Intelligence. Basel: FSB. https://www.fsb.org/2024/11/the-financial-stability-implications-of-artificial-intelligence/.
Machado, M. R., Chen, D. T., & Osterrieder, J. R. (2025). An analytical approach to credit risk assessment using machine learning models. Decision Analytics Journal, 16, 100605. https://doi.org/10.1016/j.dajour.2025.100605.
Mawardi, I., Estetiono, A., Widiastuti, T., Robani, A., Al Mustofa, M. U., Hakim, F. K., Almaulidiyah, Q., & Dewi, E. P. (2025). Hybrid early warning system: Integration of Z-score and machine learning for predicting financial performance of IRB in Indonesia. Journal of Open Innovation: Technology, Market, and Complexity, 100694. https://doi.org/10.1016/j.joitmc.2025.100694.
Tarkocin, C., & Donduran, M. (2024). Constructing early warning indicators for banks using machine learning models. The North American Journal of Economics and Finance, 69(B), 102018.
https://doi.org/10.1016/j.najef.2023.102018.
Абдурахмонов И.Х. (2022) Суғурта соҳасида рақамли технологияларни қўллаш истиқболари. “Moliya va bank ishi” илмий журнал. №1, 95-99 б.
Абдурахмонов, И. (2024). Неизвестные нам риски цифровой экономики. Экономическое развитие и анализ, 2(7), 230–239. извлечено от https://inlibrary.uz/index.php/eitt/article/view/48792.
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