From Data to Decision: Leveraging Machine Learning for Crisis Response

Abstract

This study explores the integration of machine learning (ML) techniques in early warning models (EWMs) for financial crises, emphasizing decision-making in policy contexts. By com paring traditional statistical models such as logistic regression and advanced ML techniques, including boosting methods such as AdaBoost, Gradient Boosting, XGBoost, LightGBM, and CatBoost, the research evaluates predictive accuracy and decision-making efficiency. Utilizing a dataset spanning 13 countries over 49 years, this paper highlights key economic indicators such as Account-to-GDP, Inflation, and Housing Price Cycles as critical predictors. The findings underscore the superior performance of boosting models and provide actionable insights for policymakers on optimizing thresholds (τ) and balancing predictive error through relative preference (μ). Specifically, the analysis demonstrates how varying τ and μ influences model effectiveness, highlighting the trade-offs between Type I and Type II errors. This research contributes to enhancing financial stability through informed crisis anticipation and proactive policy interventions.