SUN'IY INTELLEKT TEXNOLOGIYALARINING MOLIYAVIY XIZMATLAR VA BANK TIZIMI SAMARADORLIGIGA TA'SIRI

Authors

  • NORTOJIYEV SHOHJAXON ABDUHAMIDOVICH Author
  • SHIRINOVA SHAHNOZA ABDINABIYEVNA Author

Keywords:

sun'iy intellekt, moliyaviy xizmatlar, bank tizimi, mashinali o'rganish, kredit tahlili, firibgarlikni aniqlash, FinTech, risk-menejment, raqamli bank, samaradorlik.

Abstract

Ushbu maqolada sun'iy intellekt (SI) texnologiyalarining 
moliyaviy xizmatlar va bank tizimi samaradorligiga ta'siri keng qamrovli ilmiy tahlil 
asosida o'rganilgan. Tadqiqotda mashinali o'rganish (machine learning), tabiiy tilni 
qayta ishlash (NLP), chuqur o'rganish (deep learning) va neyron tarmoq 
algoritmlarining kredit tahlili, firibgarlikni aniqlash, risk-menejment va mijozlarga 
xizmat ko'rsatish jarayonlaridagi o'rni ishlab chiqilgan. Olingan natijalar shuni 
ko'rsatadiki, SI texnologiyalari operatsion xarajatlarni 30% gacha kamaytirish, kredit 
tasdiqlov jarayonini 85% ga tezlashtirish va moliyaviy firibgarlik hollarini 50% ga 
qisqartirish imkonini beradi. Maqola O'zbekiston bank tizimiga SI texnologiyalarini 
joriy etish bo'yicha amaliy tavsiyalar bilan yakunlanadi. 

References

1. Arner, D. W., Barberis, J., & Buckley, R. P. (2016). The evolution of Fintech:

A new post-crisis paradigm? Georgetown Journal of International Law, 47(4), 1271

1319.

2. Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review.

Statistical Science, 17(3), 235–255. https://doi.org/10.1214/ss/1042727940

3. Buchanan, B. (2019). Artificial intelligence in finance. The Alan Turing

Institute. https://doi.org/10.5281/zenodo.2565228

4. Cao, L. (2022). AI in finance: Challenges, techniques, and opportunities.

ACM Computing Surveys, 55(3), 1–38. https://doi.org/10.1145/3502289

5. Chui, M., Hazan, E., Roberts, R., Singla, A., Smaje, K., Sukharevsky, A., &

Zemmel, R. (2023). The economic potential of generative AI: The next productivity

frontier. McKinsey Global Institute.

6. Dong, X., Liu, P., & Ma, F. (2022). Financial sentiment analysis based on

pre-trained language models. Expert Systems with Applications, 190, 116155.

https://doi.org/10.1016/j.eswa.2021.116155

7. Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. C. (2015).

Benchmarking state-of-the-art classification algorithms for credit scoring: An update

of research. European Journal of Operational Research, 247(1), 124–136.

8. Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey

of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.

9. Toshmatov, B. A., & Nazarov, D. S. (2023). Raqamli bank xizmatlarini

rivojlantirishda innovatsion texnologiyalarning o'rni. O'zbekiston iqtisodiy jurnali,

12(3), 45–58.

10. World Economic Forum. (2023). The Future of Financial Services: How

Disruptive Innovations Are Reshaping the Way Financial Services Are Structured,

Provisioned and Consumed. WEF Report.

Published

2026-06-17

How to Cite

SUN’IY INTELLEKT TEXNOLOGIYALARINING MOLIYAVIY XIZMATLAR VA BANK TIZIMI SAMARADORLIGIGA TA’SIRI. (2026). ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ, 95(3), 413-425. https://alpharesearchs.com/index.php/obr/article/view/2535