SUN’IY INTELLEKT VA PREDIKTIV ANALITIKA ASOSIDA KORXONALARDA KREDITGA LAYOQATLILIKNI BAHOLASHNING RAQAMLI MODELI
Abstract
Raqamli texnologiyalar va sun’iy intellekt asosidagi kredit skoring tizimlari korxonalarda risklarni aniqlash va boshqarishni yangi bosqichga olib chiqadi. An’anaviy modellar cheklovli va kechikkan bo‘lsa, AI va big data tahlili real vaqtda ma’lumotlarni tahlil qilib, aniq va tez qarorlar qabul qilinishini ta’minlaydi. Ushbu maqolada kreditga layoqatlilikni baholash, risklarni prognozlash va portfelni optimallashtirish bo‘yicha AI usullari, ularning afzalliklari va cheklovlari tahlil qilinadi. Natijalar kredit siyosatini takomillashtirish va moliyaviy barqarorlikni oshirish bo‘yicha takliflar beradi.References
- Altman, E. I. (2018). Corporate financial distress prediction and credit risk modeling. Journal of Credit Risk, 14(4), 1–23
- Beaver, W. H., McNichols, M., & Rhie, J. (2020). Credit risk assessment and financial ratio analysis in modern conditions. Accounting Review, 95(3), 45– 67
- 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
- Thomas, L. C., Crook, J., & Edelman, D. (2017). Credit scoring and its applications. SIAM Press
- Wang, G., Ma, J., & Huang, L. (2021). AI-based predictive analytics for creditworthiness assessment in digital finance. Expert Systems with Applications, 168, 114–225
- Zhang, Y., & Zhao, X. (2019). Big Data and real-time credit risk evaluation in corporate finance. International Journal of Financial Studies, 7(2), 35
Downloads
Download data is not yet available.