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Comparative Analysis Of Digital Financial Technologies For Credit Risk Prediction And Management In Commercial Banks Based On Artificial Intelligence And Big Data Analytics

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  • Kholdorov Sardor Umarovich

Abstract

This article examines modern digital approaches to credit risk prediction and management incommercial banks based on artificial intelligence and big data analytics. The effectiveness of machine learningmodels such as XGBoost, Random Forest, and neural networks is analyzed in comparison with traditionalstatistical models, particularly logistic regression. Based on real statistical data, it is demonstrated that theaccuracy of the models increased by 25%, while the default rate decreased by 20–30%.

Suggested Citation

  • Kholdorov Sardor Umarovich, 2026. "Comparative Analysis Of Digital Financial Technologies For Credit Risk Prediction And Management In Commercial Banks Based On Artificial Intelligence And Big Data Analytics," GREEN ECONOMY AND DEVELOPMENT, "Ma'rifat-Print-Media" LLC, Tashkent State University of Economics, vol. 4(7), pages 26-32, July.
    • Handle: RePEc:teu:ged000:v:4:y:2026:i:7:id:11523
    DOI: 10.5281/zenodo.21273181
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