OPPORTUNITIES FOR USING MACHINE LEARNING METHODS IN CREDIT RISK ASSESSMENT IN COMMERCIAL BANKS
Yuldasheva Anasaliyevna UmidaScientific Advisor, Tashkent State University of Economics Professor of the Department of International Finance, DScDiyora Obitjon kizi Xo'jaqulovaTashkent State University of Economics Student of the Faculty of "Digital Economy and Information Technologies"Sitora Nozim kizi NormamatovaTashkent State University of Economics Student of the Faculty of "Finance"
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This article examines the possibilities of using machine learning methods in credit risk assessment for commercial banks. Based on the latest statistical data from Uzbekistan's banking sector, the study compares traditional credit scoring models with modern algorithms - Random Forest, XGBoost, and logistic regression. The findings indicate that ensemble methods provide superior predictive accuracy compared to traditional approaches. The article concludes with practical recommendations for improving credit risk management in the context of digital transformation of Uzbekistan's commercial banks.
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