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

Posted: May 28, 2024

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

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 financial 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, significantly 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 financial analysis and modern computational approaches in emerging market contexts.