SUN’IY INTELLEKT ASOSIDA MOLIYAVIY STRESS-TEST TIZIMINI YARATISH METODOLOGIYASI

SUN’IY INTELLEKT ASOSIDA MOLIYAVIY STRESS-TEST TIZIMINI YARATISH METODOLOGIYASI

Authors

  • Ismoil Zaynutdinov

DOI:

https://doi.org/10.5281/zenodo.17657694

Keywords:

Sun’iy intellekt, moliyaviy barqarorlik, stress-test, prognozlash, iqtisodiy xavfsizlik, LSTM, XGBoost

Abstract

Ushbu maqolada moliyaviy barqarorlikni baholashning zamonaviy vositasi sifatida sun’iy intellekt asosida
stress-test tizimini yaratish metodologiyasi ishlab chiqilgan. Tadqiqotda iqtisodiy o‘zgaruvchanlik sharoitida bank sektori
va investitsion faoliyat barqarorligini baholash uchun LSTM va XGBoost modellarining kombinatsiyasi taklif etiladi.
Natijalar shuni ko‘rsatadiki, sun’iy intellektga asoslangan yondashuvlar moliyaviy tizimdagi zaif nuqtalarni erta aniqlash,
risklarni prognozlash va iqtisodiy siyosat samaradorligini oshirish imkonini beradi. Taklif etilgan metodologiya O‘zbekiston
iqtisodiy tizimida raqamli moliyaviy nazoratni takomillashtirishda amaliy ahamiyat kasb etadi

Author Biography

Ismoil Zaynutdinov

Toshkent davlat sharqshunoslik universiteti
Iqtisodiyot va menejment kafedrasi dotsenti, PhD

References

Zaynutdinov, I. S. (2025). Korxonalarning bankrotlik ehtimolini sun’iy intellekt yordamida prognozlash. Yashil Iqtisodiyot

va Taraqqiyot, 3(11), 11–16.

Zaynutdinov, I. S. (2025). Sun’iy intellekt yordamida investitsion xavflarni baholash metodologiyasi. XXI asr: fan va

taʼlim masalalari ilmiy elektron jurnali, №3, ISSN 2181-9874.

Kongratbay Sharipov. (2025). Amalga oshirishning ustuvor yo‘nalishlari – oliy ta’lim transformatsiyasi, raqamli

modernizatsiya va barqaror rivojlanish omillari. Yashil Iqtisodiyot va Taraqqiyot, 3(1), 14–16.

Axmedjanov, K. (2020). Ахмеджанов Каримжон. Rискlarni korporativ boshqarishda ichki audit. Архив научных

исследований, 1(4).

Eshov, M. (2020). Influence assessment of enterprise management value based on coefficients methods under the

risk conditions. Advances in Mathematics: Scientific Journal, 9(9), 7573–7598.

OECD. (2024). Artificial Intelligence in Financial Risk Assessment. Paris: OECD Publishing.

World Bank. (2023). Digital Finance and Economic Resilience in Central Asia. Washington, DC: World Bank Group.

IMF. (2023). Artificial Intelligence and the Future of Macroeconomic Policy. Washington, DC: International Monetary

Fund.

Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal

of Finance, 23(4), 589–609.

Zhang, L., & Li, W. (2022). Machine Learning Models for Financial Distress Prediction in Emerging Economies. Journal

of Sustainable Finance & Investment, 12(4), 455–471.

Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD

International Conference on Knowledge Discovery and Data Mining, 785–794.

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Published

2025-11-01
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