O‘ZBEKISTON TIJORAT BANKLARIDA KREDIT RISKINI ERTA ANIQLASH TIZIMINI SUN’IY INTELLEKT ASOSIDA TAKOMILLASHTIRISH
DOI:
https://doi.org/10.5281/zenodo.22864614Abstract
Maqolada O‘zbekiston tijorat banklarida kredit riskini NPL shakllanishidan oldin aniqlash uchun sun’iy intellektga asoslangan erta ogohlantirish yondashuvi taklif etiladi. Markaziy bankning 2023–2026-yillardagi ochiq ma’lumotlari hamda 2026-yil 1-yanvar va 1-iyul holatidagi davlat ulushi mavjud 9 bank ko‘rsatkichlari tahlil qilindi. 2026-yilning birinchi yarim yilligida jami kreditlar 6,4 foiz, NPL hajmi 27,1 foiz oshib, NPL ulushi 3,0 foizdan 3,6 foizga ko‘tarildi. Natijalar asosida qoidaviy triggerlar, logistika regressiyasi, mashinaviy o‘rganish, SHAP izohlari, model drift nazorati va ekspert ko‘rigini birlashtiruvchi ikki bosqichli EWS modeli taklif qilindi.Keywords
muammoli aktivlar, defolt ehtimoli, xulqiy skoring, SHAP qiymatlari, model drift, portfel monitoringiReferences
Ashofteh, A., & Bravo J., M. (2021). A conservative approach for online credit scoring. Expert Systems with Applications, 176. https://doi.org/10.1016/j.eswa.2021.114835
Bueff A., C., Cytryński, M., Calabrese, R., Jones, M., Roberts, J., Moore, J., & Brown, I. (2022). Machine learning interpretability for a stress scenario generation in credit scoring based on counterfactuals. Expert Systems with Applications, 202. https://doi.org/10.1016/j.eswa.2022.117271
Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable Machine Learning in Credit Risk Management. Computational Economics, 57, 203–216. https://doi.org/10.1007/s10614-020-10042-0
de Lange P., E., Melsom, B., Vennerød C., B., Westgaard, S., & Explainable, A. I. (2022). for Credit Assessment in Banks. Journal of Risk and Financial Management, 15(12). https://doi.org/10.3390/jrfm15120556
Engan, A., Hjelkrem L., O., & Risstad, M. (2026). A review of credit risk assessment in banking – From deterministic models to probabilistic AI. Finance Research Open, 2(2). https://doi.org/10.1016/j.finr.2026.100125
Financial Stability Board. The Financial Stability Implications of Artificial Intelligence. – Basel: FSB, 2024.
Genovesi, S., Mönig J., M., Schmitz, A., et al. (2024). Standardizing fairness-evaluation procedures: interdisciplinary insights on machine learning algorithms in creditworthiness assessments for small personal loans. AI and Ethics, 4(2), 537–553. https://doi.org/10.1007/s43681-023-00291-8
Goldmann S., H., Machado M., R., & Osterrieder J., R. (2025). Advancing credit risk assessment in the retail banking industry: A hybrid approach using time series and supervised learning models. Data & Knowledge Engineering, 160. https://doi.org/10.1016/j.datak.2025.102490
Guan, C., Suryanto, H., Mahidadia, A., Bain, M., & Compton, P. (2023). Responsible Credit Risk Assessment with Machine Learning and Knowledge Acquisition. Human-Centric Intelligent Systems, 3, 232–243. https://doi.org/10.1007/
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. https://doi.org/10.1016/j.dajour.2025.100605
Moscato, V., Picariello, A., & Sperlí, G. (2021). A benchmark of machine learning approaches for credit score prediction. Expert Systems with Applications, 165. https://doi.org/10.1016/j.eswa.2020.113986
O‘zbekiston Respublikasi Markaziy banki. Tijorat banklarining muammoli kreditlari (NPL) to‘g‘risida 2023-yil 1-yanvar, 2024-yil 1-yanvar, 2025-yil 1-yanvar, 2026-yil 1-yanvar va 2026-yil 1-iyul holatiga ma’lumotlar. – Toshkent, 2023– 2026. – URL: https://cbu.uz/uz/statistics/bankstats/ (murojaat sanasi: 12.09.2026).
O‘zbekiston Respublikasi Prezidentining 2024-yil 14-oktabrdagi PQ-358-son qarori. “Sun’iy intellekt texnologiyalarini 2030-yilga qadar rivojlantirish strategiyasini tasdiqlash to‘g‘risida”. – Qonunchilik ma’lumotlari milliy bazasi, 2024.
Shi, S., Tse, R., Luo, W., D’Addona, S., & Pau, G. (2022). Machine learning-driven credit risk: a systemic review. Neural Computing and Applications, 34(17), 14327–14339. https://doi.org/10.1007/s00521-022-07472-2
Suhadolnik, N., & Ueyama, J. (2023). Da Silva S. Machine Learning for Enhanced Credit Risk Assessment: An Empirical Approach. Journal of Risk and Financial Management, 16(12). https://doi.org/10.3390/jrfm16120496
Zedda, S. (2024). Credit scoring: Does XGBoost outperform logistic regression? A test on Italian SMEs. Research in International Business and Finance, 70. https://doi.org/10.1016/j.ribaf.2024.102397
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