Dynamic Credit Risk Assessment in Emerging Markets: A Machine Learning Framework for Banking Institutions

Abstract

This research develops a comprehensive machine learning framework
for dynamic credit risk assessment in emerging markets, addressing the
limitations of traditional models in volatile economic environments. Using a dataset of 15,000 loan applications from banking institutions across
Southeast Asia and Latin America between 2000-2003, we implement and
compare multiple machine learning algorithms including logistic regression, random forests, and support vector machines. Our methodology
incorporates both traditional }nancial ratios and novel macroeconomic
indicators to capture the dynamic nature of credit risk in developing
economies. Results demonstrate that the ensemble random forest model
achieves 94.2% accuracy in predicting loan defaults, signi}cantly outperforming traditional credit scoring models. The framework provides banking institutions with enhanced risk assessment capabilities while maintaining interpretability through feature importance analysis. This study contributes to the risk management literature by bridging the gap between
traditional }nancial analysis and modern computational approaches in
emerging market contexts.