Machine Learning for Predicting Bank Stability: Evidence from the Middle East & North Africa Region

Abstract

Artificial intelligence (AI) has become a contentious topic in the financial services sector, especially regarding its capacity to provide more accurate bank risk predictions than the traditional econometric models. However, limited research has so far been conducted in the MENA countries. In this context, the study aims to forecast bank stability across the MENA region by first validating the variables using econometric methods, and then, by applying machine learning (ML) algorithms to develop early warning systems that would improve risk assessment and financial resilience. By using panel data from 2000 to 2020, the research analyzed a sample of 33 listed commercial banks from six MENA countries. The findings demonstrated that econometric analysis confirms the importance of conventional bank-specific and macroeconomic variables in influencing bank stability, while the ML results indicated that the Gradient Boosting machine learning model achieved the highest accuracy score, with bank profitability (ROA), asset size, solvency, unemployment, and inflation identified as the most influential features. In this regard, in order to enhance predictive accuracy and address stability concerns, the study recommends that banks in the MENA region should adopt this machine learning approach. Additionally, it advises that regulators should utilize ML insights to develop policies aimed at strengthening the financial stability of MENA banks.