Predicting Financial Distress Using Machine Learning Models: The Case of Banking Sector in Ethiopia

Abstract

This research delves into the application of machine learning models to predict financial distress within the Ethiopian commercial banking sector. The specific inquiries guiding the study's objective to build an accurate predictive model for bank financial distress. The questions seek to determine how machine learning models can be developed in the Ethiopian banking sector. The study leverages the well-established CAMEL framework, which examines Capital Adequacy, Asset Quality, Management, Earnings, and Liquidity, as well as the Altman Z-score measurements-to label distress banks, to develop a comprehensive predictive model. This study evaluates several machine learning algorithms—including Random Forest and Neural Networks—using thirty years of financial data from 18 Ethiopian commercial banks to identify distress patterns. The researchers have employed optimization techniques such as SMOTE oversampling, hyperparameter tuning, and ensemble methods to refine the models' ability to anticipate financial distress before it materializes. 

The findings demonstrate that the Random Forest model- addressed dataset imbalance & overfitting problem by applying SMOTE oversampling combined with class weighting and evaluated through stratified cross- validation demonstrated exceptional performance and achieves an overall accuracy of 97.70% and a Kappa statistic of 0.9458, significantly improving the identification of distressed banks. Followed by another ensemble method REP (Reduced Error Pruning)-decision trees ensemble method like Bagging exhibit strong predictive capabilities, with an accuracy of 96.07%. The study emphasizes the importance of data pre-processing, feature selection, and dimensionality reduction in building robust models. By integrating machine learning with the CAMEL framework, the research 
provides a data-driven approach to financial distress prediction, offering valuable insights for regulators, bank management, and investors. The study concludes with recommendations for implementing machine learning-based early warning systems, improving data quality, and fostering collaboration among stakeholders to enhance the stability and resilience of the Ethiopian banking sector.