AI Driven Fiscal Risk Assessment in the Eurozone: A Machine Learning Approach to Public Debt Vulnerability

This study applied supervised machine learning algorithms to macro-fiscal panel data from 20 EU member states (2000–2024) to model and predict fiscal stress episodes in the Eurozone. Conventional frameworks for assessing public debt sustainability often rely on static thresholds and linear dynamics, limiting their ability to capture the complex, non-linear interactions in fiscal data. To address this, we implemented logistic regression, support vector machines, and XGBoost classifiers using core fiscal indicators such as debt-to-GDP ratio, primary balance, GDP growth, interest rates, and inflation. The models were evaluated using time-aware cross-validation, with XGBoost delivering the highest predictive accuracy but showing some signs of overfitting. We highlighted the interpretability of logistic regression and applied SHAP values to enhance transparency in the tree-based models. While limited by using annual data, we discuss the potential value of incorporating real-time or high-frequency fiscal indicators. Our results underscore the practical relevance of AI-enhanced early warning systems for fiscal surveillance and support their integration into institutional monitoring frameworks.