RAQAMLI IQTISODIYOT SHAROITIDA TIJORAT BANKLARIDA KREDIT RISKINI SUN’IY INTELLEKT ASOSIDA ERTA ANIQLASH JARAYONLARINI TAKOMILLASHTIRISH
pdf

Ключевые слова

raqamli iqtisodiyot, raqamli transformatsiya, sun’iy intellekt, kredit riski, ma’lumotlar integratsiyasi, erta ogohlantirish tizimi, izohlanuvchi mashinaviy o‘rganish, raqamli platforma.

Аннотация

Tezisda raqamli iqtisodiyot sharoitida tijorat banklarida kredit riskini kechikish yuzaga kelishidan oldin aniqlash uchun sun’iy intellektdan foydalanishning tashkiliy-uslubiy mexanizmi taklif etiladi. O‘zbekiston bank tizimining ochiq statistikasi va xalqaro ilmiy manbalar asosida kredit, hisob-kitob va monitoring tizimlari ma’lumotlarini yagona raqamli platformada integratsiyalash, xulqiy belgilarni shakllantirish, izohlanuvchi modelni validatsiya qilish hamda signalni boshqaruv qaroriga aylantirish jarayonlari asoslangan. Mexanizmning samarasi faqat model aniqligi bilan emas, ma’lumotlar almashinuvi tezligi, riskni oldindan aniqlash davri va kredit monitoringi xarajatlarining qisqarishi bilan baholanadi.

pdf

Библиографические ссылки

Ayari H., Guetari R., Kraiem N. Machine learning powered financial credit scoring: a systematic literature review // Artificial Intelligence Review. 2026. Vol. 59. Art. 13. DOI: 10.1007/s10462-025-11416-2.

Basel Committee on Banking Supervision. Principles for the Management of Credit Risk. Basel: Bank for International Settlements, 2025.

Bhatore S., Mohan L., Reddy Y.R. Machine learning techniques for credit risk evaluation: a systematic literature review // Journal of Banking and Financial Technology. 2020. Vol. 4. P. 111-138. DOI: 10.1007/s42786-020-00020-3.

Bussmann N., Giudici P., Marinelli D., Papenbrock J. Explainable Machine Learning in Credit Risk Management // Computational Economics. 2021. Vol. 57. P. 203-216. DOI: 10.1007/s10614-020-10042-0.

Central Bank of the Republic of Uzbekistan. Information on major indicators of commercial banks as of July 1, 2026. Tashkent, 2026. URL: https://cbu.uz/en/statistics/bankstats/4230544/ (accessed: 13.09.2026).

European Banking Authority. Follow-up Report on the Use of Machine Learning for Internal Ratings-Based Models. Paris, 2023.

Hlongwane R., Ramabao K., Mongwe W. A novel framework for enhancing transparency in credit scoring: Leveraging Shapley values for interpretable credit scorecards // PLOS ONE. 2024. Vol. 19(8). e0308718. DOI: 10.1371/journal.pone.0308718.

Lundberg S.M., Lee S.-I. A Unified Approach to Interpreting Model Predictions // Advances in Neural Information Processing Systems. 2017. Vol. 30. P. 4765-4774.

Perez-Cruz F., Prenio J., Restoy F., Yong J. Managing explanations: how regulators can address AI explainability // FSI Occasional Papers. 2025. No. 24. Basel: BIS.

Ribeiro M.T., Singh S., Guestrin C. Why Should I Trust You? Explaining the Predictions of Any Classifier // Proceedings of KDD 2016. P. 1135-1144. DOI: 10.1145/2939672.2939778.

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. – URL: https://lex.uz/docs/-7158604.