What question did this study set out to answer?

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

Key Points

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.

Abstract

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.

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

Key Points

Abstract

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