Banking crises pose a constant threat to macroeconomic stability in emerging markets, where standard econometric Early Warning Systems (EWS) often fail to model nonlinear macro-financial relationships. This paper examines whether machine learning algorithms, rather than standard logistic regression, can improve forecasts of banking crisis risk in Nigeria. We compare the performance of Random Forests, Support Vector Machines (SVMs), and Extreme Gradient Boosting (XGBoost) to logistic regression on the African Financial Crises dataset (1954-2014) with annual data. Resampling is restricted to the training set to compensate for the rarity of crisis instances. In a strict out-of-time validation setting, the model’s accuracy is assessed by accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC). Our findings show that tree-based ensemble models outperformed logistic regression on the test set: XGBoost generalises better (AUC = 1.0; F1 = 0.95 for non-crisis, 0.80 for crisis) instances, although Random Forest yields the highest cross-validated F1-score on the training set. Exchange rate volatility, inflation, systemic crisis variables, and defaults on external sovereign debt are identified as key predictors through feature importance analysis. Crisis years exhibit the strongest predictive signals, suggesting that annual data have limited early-warning capacity. Due to the small sample size and lack of crisis observations during the test period, results should be interpreted cautiously. All things considered, the findings provide strong early evidence that, although not yet ready as fully functional policy tools, machine learning models can support conventional tools for tracking banking crises in Nigeria.

Banking crises are a persistent threat to macroeconomic stability in emerging markets, where conventional econometric monitoring frameworks often fail to capture non-linear macro-financial relationships. This paper examines whether machine learning algorithms can improve the detection of banking crisis risk in Nigeria compared to standard logistic regression. We compare the performance of Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) against logistic regression using annual data from the African Financial Crises dataset (1954–2014). Resampling is only implemented on the training set to overcome the infrequency of crisis events. Performance on models is assessed based on accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC) in a rigorous out-of-time validation setting. Our findings indicate that tree-based ensemble models outperform logistic regression on the test set: XGBoost achieves the best generalization performance (AUC = 1.0; F1 = 0.95 in non-crisis, 0.80 in crisis), whereas Random Forest has the highest cross-validated F1-score on the training set. The most important variables are exchange rate volatility, inflation, and indicators of systemic crisis. The most significant crisis indicators are, however, seen in crisis years, which means that the annual data do not provide much lead-time to detect the crisis. These results should be taken with caution because of the small sample size and the limited number of crisis observations during the test period. Altogether, machine learning models have potential as additional tools to monitor banking crises in Nigeria, though at the moment they are not fully operational as policy instruments.