The article examines the application of artificial intelligence to forecasting credit losses and market volatility in commercial banks. It analyses traditional statistical methods and machine-learning algorithms used to estimate default probability, expected credit losses, and financial market dynamics. The study substantiates the use of a hybrid approach combining econometric models with intelligent algorithms. The main limitations associated with data quality, model explainability, and model risk are also identified.
| Mualliflar | Kadirova , Barno, Кадирова , Барно, Kadirova , Barno |
|---|---|
| Jurnal | Иқтисодий тараққиёт ва таҳлил |
| Nashr sanasi | 2026-07-31 |
| Jild | 4 |
| Son | 7 |
| Betlar | 226-233 |
| Til | Rus |
| DOI | 10.60078/2992-877x-2026-vol4-iss7-pp226-233 |
DOI: 10.60078/2992-877x-2026-vol4-iss7-pp226-233 · Maqolaning asl sahifasi
artificial intelligence, commercial banks, credit risk, credit losses, market risk, market volatility, machine learning, risk management, искусственный интеллект, коммерческие банки, кредитный риск, кредитные потери, рыночный риск, рыночная волатильность, машинное обучение, риск менеджмент, sun’iy intellekt, tijorat banklari, kredit riski, kredit yo‘qotishlari, bozor riski, bozor volatilligi, mashinaviy o‘qitish, risk-menejment
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