COMPARATIVE ANALYSIS OF DIGITAL FINANCIAL TECHNOLOGIES FOR CREDIT RISK PREDICTION AND MANAGEMENT IN COMMERCIAL BANKS BASED ON ARTIFICIAL INTELLIGENCE AND BIG DATA ANALYTICS

Kholdorov Sardor Umarovich

GREEN ECONOMY AND DEVELOPMENT, 2026, vol. 4, issue 7, 26-32

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%.

Keywords: artificial intelligence; big data; credit risk; machine learning; XGBoost; digital financial technologies; predictive modeling. (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:teu:ged000:v:4:y:2026:i:7:id:11523

DOI: 10.5281/zenodo.21273181

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