O‘ZBEKISTON TIJORAT BANKLARI UCHUN SUN’IY INTELLEKTGA ASOSLANGAN KREDIT RISKI VA LIKVIDLILIKNI BOSHQARISH
DOI:
https://doi.org/10.65451/iqtisodiyotuzb.v11i13.14131Аннотация
Ushbu maqolada O‘zbekiston tijorat banklarida kredit riski va likvidlilikni boshqarishda sun’iy intellekt texnologiyalarining samaradorligi empirik ma’lumotlar asosida baholanadi. Maqolada bank risklarini real vaqt rejimida aniqlash, prognoz qilish va boshqarish imkonini beruvchi AI-integratsiyalashgan modelni shakllantirishga, shuningdek, metodologik yondashuv sifatida mashinali o‘rganish asosida kredit riskini baholash, shuningdek, LSTM va GRU arxitekturalari orqali likvidlilikni prognozlash usullari qo‘llanilgan.
Библиографические ссылки
1. CBU (Central Bank of Uzbekistan). (2024). Banking System Liquidity Indicators 2020–2024. Tashkent.
2. BIS. (2025). Artificial Intelligence and Machine Learning in Financial Services: Policy Implications. Basel.
3. World Bank. (2023). Digital Financial Infrastructure and Risk Management in Emerging Economies. Washington.
4. IMF. (2023). Fintech and Artificial Intelligence in Financial Stability Assessments. Washington, D.C.
5. Federal Reserve System. (2022). Liquidity Stress Testing in U.S. Banks. Washington, D.C.
6. Bundesbank Research. (2023). Credit Risk Modelling Using Gradient Boosting Techniques in German Banks. Frankfurt am Main.
7. ING Analytics Lab. (2024). Machine-Learning Approaches to Credit Risk Modelling in European Banks. Amsterdam: ING Research.
8. Zhang, L., & Li, C. (2022). Machine learning models for credit default prediction: Evidence from Asian emerging markets. Finance Research Letters, 48, 102983.
9. Singh, R., Kumar, A., & Bhatia, S. (2023). AI-Driven Credit Scoring Models: Accuracy, Bias and Regulatory Challenges. Journal of Financial Regulation and Compliance, 31(2), 145–162.
10. European Central Bank (ECB). (2021). Liquidity Stress Testing Methodology. Frankfurt: ECB.
11. Prudential Regulation Authority (PRA). (2023). Liquidity Risk Management under AI-Supported Systems. London: Bank of England.
12. Deutsche Bank Research. (2023). NSFR Forecasting Using GRU Models. Frankfurt.
13. ING Bank. (2022). Forecasting LCR Using LSTM Neural Networks. Amsterdam.
14. Berger, A. N., & Bouwman, C. H. S. (2022). Bank liquidity creation, capital structure and financial stability. Journal of Banking & Finance, 141, 106501.
15. Tran, K., Bui, T., & Nguyen, H. (2023). Liquidity hoarding and credit contraction in emerging markets during COVID-19. Emerging Markets Review, 55, 100974.
16. Chen, J., Wang, Y., & Luo, M. (2024). Systemic Liquidity Risk Measurement Using Deep Neural Networks. Journal of Financial Stability, 72, 101205.
17. Guerra, F., Marin, M., & Lopez, R. (2022). Liquidity risk evaluation using Random Forest approaches. Expert Systems with Applications, 204, 117597.
18. OECD. (2023). Responsible and Explainable AI in the Financial Sector. Paris.
19. Ministry of Economy and Finance of Uzbekistan. (2024). Financial Sector Digitalization Report. Tashkent.
