COMPARATIVE ANALYSIS OF DIGITAL FINANCIAL TECHNOLOGIES FOR CREDIT RISK PREDICTION AND MANAGEMENT IN COMMERCIAL BANKS BASED ON ARTIFICIAL INTELLIGENCE AND BIG DATA ANALYTICS
DOI:
https://doi.org/10.5281/zenodo.21273181Keywords:
artificial intelligence, big data, credit risk, machine learning, XGBoost, digital financial technologies, predictive modeling.Abstract
This article examines modern digital approaches to credit risk prediction and management in
commercial banks based on artificial intelligence and big data analytics. The effectiveness of machine learning
models such as XGBoost, Random Forest, and neural networks is analyzed in comparison with traditional
statistical models, particularly logistic regression. Based on real statistical data, it is demonstrated that the
accuracy of the models increased by 25%, while the default rate decreased by 20–30%.
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