Data-Driven Framework for Credit Portfolio Performance Monitoring and Early Warning Signals

Effective credit portfolio management is essential for maintaining financial stability and minimizing credit losses in banking and financial institutions. This study proposes a Data-Driven Framework for Credit Portfolio Performance Monitoring and Early Warning Signals aimed at enhancing the identification and management of emerging credit risks. The framework integrates borrower behavioral data, portfolio performance indicators, macroeconomic variables, and advanced predictive analytics techniques to continuously assess portfolio health and detect potential default risks at an early stage. A hypothetical dataset comprising loan-level information was utilized to develop and evaluate the framework. Multiple predictive models, including Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting, were employed to estimate default probabilities and generate risk alerts. The results demonstrated significant improvements in portfolio performance, including reductions in delinquency rates, default rates, portfolio-at-risk levels, and non-performing asset ratios, along with enhanced recovery rates. Among the evaluated models, Gradient Boosting achieved the highest predictive performance with an accuracy of 91.1% and an AUC-ROC score of 0.95. The Early Warning Signal system successfully identified a substantial proportion of high-risk borrowers prior to default occurrence, enabling timely intervention and risk mitigation. The findings highlight the importance of integrating predictive analytics and continuous monitoring mechanisms into credit risk management practices.