Detecting Credit Risk in Egyptian Banks: Does Machine Learning Matter?
Date
2025-05-14
Journal Title
Journal ISSN
Volume Title
Type
Article
Publisher
Vilnius University Press
Series Info
Ekonomika ; Volume 104, Issue 2, Pages 78 - 94 , 2025
Scientific Journal Rankings
Abstract
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
Description
SJR 2024
0.420 Q2
H-Index
10
Keywords
Bank-specific, corporate credit risk, Egyptian banking sector, institutional heterogeneity, machine learning algorithm, macro-financial integration, Random Forest, retail credit risk
Citation
Abdou, D. M. S., Farrag, K., & Ali, L. (2025). Detecting credit risk in Egyptian Banks: Does machine learning matter? Ekonomika, 104(2), 78–94. https://doi.org/10.15388/ekon.2025.104.2.5