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The aim is to evaluate machine learning methods for enhancing credit risk assessment in commercial banks.

June 4, 2026Open Access

Opportunities for Using Machine Learning Methods in Credit Risk Assessment in Commercial Banks

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The aim is to evaluate machine learning methods for enhancing credit risk assessment in commercial banks.
Analysis of statistical data from Uzbekistan's banking sector.
Comparison of traditional credit scoring models with machine learning algorithms such as Random Forest, XGBoost, and logistic regression.
Evaluation of predictive accuracy of ensemble methods against traditional approaches.
Ensemble methods show superior predictive accuracy compared to traditional credit scoring models.
Modern algorithms outperform traditional methods in assessing credit risk.
Recommendations are provided for enhancing credit risk management in the digital transformation context.

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This article examines machine learning methods improving credit risk assessment in commercial banks, suggesting practical enhancements.

Umida et al. (2026) studied this question.

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