Detecting Credit Risk in Egyptian Banks: Does Machine Learning Matter? - Salman Abdou, Doaa M.; Farrag, Karim; Ali, Loubna (Ekonomika, 2025)
This study aims to significantly enhance the predictive modeling of credit risk within Egypt’s banking sector, particularly by differentiating between retail and corporate credit risks and categorizing banks into listed and non-listed groups. By utilizing a comprehensive dataset from Middle Eastern countries spanning 2011 to 2023, the research applies advanced machine learning techniques, including the Random Forest algorithm, to refine the predictive model.The novelty of this research lies in its detailed exploration of credit risk determinants specific to the Egyptian banking sector, providing valuable insights into emerging economies. A distinction between various types of credit risk and bank classifications is made. The findings reveal that bank-specific factors – such as the asset size, the operating efficiency, the liquidity, the income diversification, and the capital adequacy – are more significant predictors of credit risk than macroeconomic indicators. This trend holds for both listed and non-listed banks, thus highlighting the importance of internal metrics.Moreover, the Random Forest algorithm demonstrates a high accuracy rate in predicting credit risk exposures, which underscores the effectiveness of machine learning in financial settings. The analysis indicates that variations in the asset size, operating efficiency, and other characteristics are crucial in influencing retail and corporate credit risks. These insights suggest that prioritizing internal bank metrics could lead to more effective credit risk management strategies than relying solely on external economic conditions.Ultimately, this study’s predictive model is expected to enhance credit risk assessment capabilities, strengthening the financial positions of banks and fostering economic growth in the region. By bridging the gap between theoretical understanding and practical application, this research offers a novel perspective on credit risk management tailored to the unique context of the Egyptian banking sector.
Keywords: Bank-specific; Retail credit risk; Corporate Credit Risk; Machine Learning Algorithm; Egyptian banking sector; Random Forest; institutional heterogeneity; macro-financial integration