Keywords
moliya
mashinaviy o'qitish
kredit skoringi
aktivlar narxlanishi
model riski
tizimli risk
SI boshqaruvi. 1.
How to Cite
Abstract
Maqolada moliya sohasida sun'iy intellekt (SI) texnologiyalarining qo'llanish darajasi, ularning iqtisodiy samaradorligi bo'yicha empirik dalillar, yuzaga kelayotgan risklar va tartibga solish muhiti qiyosiy-tahliliy usulda o'rganiladi. Tahlil Angliya Banki va Moliyaviy xatti-harakatlar nazorati organi (FCA) 2024 yilgi so'rovi, Xalqaro valyuta jamg'armasining Global moliyaviy barqarorlik hisobotlari (2024–2026), Gu, Kelly va Xiu (2020) tadqiqoti, Yevropa Ittifoqining SI to'g'risidagi Reglamenti hamda O'zbekistonning SI va fintex strategiyalariga tayanadi. Natijalar shuni ko'rsatadiki: (1) SI qabul qilinishi tez o'smoqda (Buyuk Britaniyada 2022 yildagi 58% dan 2024 yilda 75% gacha); (2) mashinaviy o'qitish aktivlar daromadini bashorat qilishda chiziqli modellarga nisbatan statistik va iqtisodiy jihatdan sezilarli ustunlik beradi, biroq bu ustunlik amaliyotda tranzaksiya xarajatlari va modelning eskirishi bilan cheklanadi; (3) asosiy risklar modelni tushunmaslik, uchinchi tomon provayderlariga bog'liqlik va bozor xatti-harakatlarining sinxronlashuvidir. Maqolada banklar va regulyator uchun risk–nazorat–KPI asosidagi boshqaruv doirasi taklif etiladi.References
1. Akerlof, G. A. (1970). The Market for «Lemons»: Quality Uncertainty and the Market Mechanism. Quarterly Journal of Economics, 84(3), 488–500.
2. Bank of England & Financial Conduct Authority (2024). Artificial intelligence in UK financial services – 2024. London, 21 November 2024. bankofengland.co.uk.
3. Board of Governors of the Federal Reserve System (2011). Supervisory Guidance on Model Risk Management (SR 11-7).
4. European Union (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (AI Act). Official Journal of the EU.
5. Fuster, A., Goldsmith-Pinkham, P., Ramadorai, T., Walther, A. (2022). Predictably Unequal? The Effects of Machine Learning on Credit Markets. Journal of Finance, 77(1), 5– 47.
6. Grossman, S. J., Stiglitz, J. E. (1980). On the Impossibility of Informationally Efficient Markets. American Economic Review, 70(3), 393–408.
7. Gu, S., Kelly, B., Xiu, D. (2020). Empirical Asset Pricing via Machine Learning. Review of Financial Studies, 33(5), 2223–2273. doi:10.1093/rfs/hhaa009.
8. International Monetary Fund (2024). Global Financial Stability Report, October 2024: Steadying the Course: Uncertainty, Artificial Intelligence, and Financial Stability, Chapter 3. Washington, DC.
9. International Monetary Fund (2026). Global Financial Stability Report, April 2026, Chapter 1. Washington, DC.
10. Khandani, A. E., Kim, A. J., Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767–2787.
11. Stiglitz, J. E., Weiss, A. (1981). Credit Rationing in Markets with Imperfect Information. American Economic Review, 71(3), 393–410.
12. O'zbekiston Respublikasi Vazirlar Mahkamasi/Prezidenti, RP-358-son (2024-yil 14-oktyabr). Sun'iy intellekt texnologiyalarini 2030-yilgacha rivojlantirish strategiyasi. lex.uz/en/docs/7159258.
13. O'zbekiston Respublikasi Markaziy banki (2025–2026). Press-relizlar: «Sun'iy intellekt texnologiyalari bo'yicha malaka oshirish kurslari»; «AI Challenge». cbu.uz.
14. UzDaily (2026). Uzbekistan approves fintech development strategy through 2030. uzdaily.uz.
15. OECD.AI Policy Navigator. Uzbekistan: The Strategy for the Development of Artificial Intelligence Technologies until 2030. oecd.ai.
16. Freshfields (2026). EU AI Act unpacked #34: The final Digital Omnibus on AI; DLA Piper GENIE (2026). The Digital AI Omnibus: deferral of high-risk AI obligations.