TIJORAT BANKLARIDA MUAMMOLI KREDITLARNI ANIQLASH VA KAMAYTIRISHDA SUN’IY INTELLEKT TEXNOLOGIYALARIDAN FOYDALANISH MEXANIZMLARI
DOI:
https://doi.org/10.5281/zenodo.20776436Ключевые слова:
muammoli kreditlar, NPL, sun’iy intellekt, mashinaviy o‘qitish, erta ogohlantirish tizimi, defolt ehtimoli, kutilayotgan yo‘qotish, xulq-atvor tahlili, kreditlarni undirish, risklarni boshqarish, tijorat banklariАннотация
Tijorat banklarida muammoli kreditlarni aniqlash va kamaytirishda sun’iy intellekt
texnologiyalaridan foydalanish mexanizmlari tadqiq etilgan. Tadqiqot doirasida mashinaviy o‘qitish modellari,
xulq-atvor tahlili, anomaliyalarni aniqlash hamda erta ogohlantirish tizimlarining muammoli kreditlarni
boshqarishdagi o‘rni baholangan. An’anaviy va sun’iy intellekt asosidagi yondashuvlarning samaradorligi
miqdoriy ko‘rsatkichlar asosida qiyosiy tahlil qilingan. Defolt ehtimoli, kutilayotgan yo‘qotish hamda logistik
regressiya modellarining matematik asoslari yoritilib, sun’iy intellektga asoslangan muammoli kreditlarni
boshqarish tizimining ishlash mexanizmi ishlab chiqilgan. Muammoli kreditlar ulushini qisqartirish va kredit
risklarini samarali boshqarishga qaratilgan ilmiy-amaliy takliflar ishlab chiqilgan
Библиографические ссылки
1. O‘zbekiston Respublikasi Prezidentining 2020-yil 5-oktabrdagi ““Raqamli Oʻzbekiston — 2030”
strategiyasini tasdiqlash va uni samarali amalga oshirish chora-tadbirlari toʻgʻrisida”gi PF–6079-sonli
Farmoni: https://lex.uz/docs/-5030957
2. O‘zbekiston Respublikasi Prezidenti. Bank tizimini isloh qilish strategiyasi to‘g‘risidagi Farmon. – Toshkent,
2020.
3. O‘zbekiston Respublikasi Markaziy banki. O‘zbekiston bank tizimi faoliyati bo‘yicha statistik byulleten. –
Toshkent: Markaziy bank, 2025.
4. Basel Committee on Banking Supervision. Prudential Treatment of Problem Assets: Definitions of Non-
Performing Exposures and Forbearance. – Basel: Bank for International Settlements, 2017. – 24 p.
5. International Financial Reporting Standards Foundation. IFRS 9 Financial Instruments. – London: IFRS
Foundation, 2014.
6. International Monetary Fund. Strategies for Resolving Non-Performing Loans in a Pandemic. – Washington,
DC: IMF, 2022.
7. Altman E.I., Marco G., Varetto F. Corporate Distress Diagnosis: Comparisons Using Linear Discriminant
Analysis and Neural Networks // Journal of Banking & Finance. – 1994. – Vol. 18, № 3. – P. 505–529.
8. Barboza F., Kimura H., Altman E. Machine Learning Models and Bankruptcy Prediction // Expert Systems
with Applications. – Elsevier, 2017. – Vol. 83. – P. 405–417.
9. Petropoulos A., Siakoulis V., Stavroulakis E., Vlachogiannakis N. Predicting Bank Insolvencies Using
Machine Learning Techniques // International Journal of Forecasting. – 2020. – Vol. 36, № 3. – P. 1092–
1113.
10. Bellotti A., Crook J. Loss Given Default Models Incorporating Macroeconomic Variables for Credit Cards //
International Journal of Forecasting. – 2012. – Vol. 28, № 1. – P. 171–182.
11. Lessmann S., Baesens B., Seow H.-V., Thomas L.C. Benchmarking State-of-the-Art Classification
Algorithms for Credit Scoring: An Update of Research // European Journal of Operational Research. –
2015. – Vol. 247, № 1. – P. 124–136.
12. Mahbobi M., Kimiagari S., Vasudevan M. Credit Risk Classification: An Integrated Predictive Accuracy
Algorithm Using Artificial and Deep Neural Networks // Annals of Operations Research. – Springer, 2023.
– Vol. 330. – P. 609–637.
13. Leo M., Sharma S., Maddulety K. Machine Learning in Banking Risk Management: A Literature Review //
Risks. – MDPI, 2019. – Vol. 7, № 1. – 22 p.
14. Xolmatov B.A. Tijorat banklarida muammoli kreditlarni boshqarish mexanizmlarini takomillashtirish //
Iqtisodiyot va innovatsion texnologiyalar. – 2024. – № 5. – 80–88-betlar.
15. Sultonova N.M. Sun’iy intellekt asosida kredit risklarini erta aniqlash // Bank-moliya tizimi. – 2025. – №
1. – 58–66-betlar.
16. Ergashev D.T. Bank kredit portfeli sifatini boshqarishda raqamli texnologiyalar // Iqtisodiy taraqqiyot va
tahlil. – 2025. – № 3. – 52–60-betlar
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