Dynamic Credit Risk Assessment in Banking: A Machine Learning Framework for Default Prediction

Posted: Oct 28, 2025

• Wei Zhang
• Maria Rodriguez
• Kenji Tanaka
• Fatima Al-Mansoori

Abstract

This research develops a comprehensive machine learning framework for dynamic credit risk assessment in commercial banking. Traditional credit scoring models often fail to capture the nonlinear relationships and temporal dependencies in financial data. We propose an integrated approach combining gradient boosting algorithms with time-series analysis to predict corporate loan defaults. Using a dataset of 15,000 corporate loans from 2000-2003 across multiple financial institutions, we evaluate the performance of our framework against conventional logistic regression and discriminant analysis models. Our results demonstrate that the gradient boosting approach achieves 94.3% accuracy in default prediction, significantly outperforming traditional methods. The model incorporates both static financial ratios and dynamic macroeconomic indicators, providing early warning signals up to 12 months before default events. This research contributes to the risk management literature by offering a more robust and adaptive framework for credit risk assessment in volatile economic environments.