Machine Learning-Based Early Warning System For Banking Crises: A Case Study Of Nigeria

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.