This study aims to develop an explainable artificial intelligence (AI)–regulatory technology (RegTech) early warning system (EWS) to predict economic and financial crises by integrating macroeconomic, financial, governance and labour-market indicators within a sustainability-aligned supervisory architecture linked to Sustainable Development Goals (SDGs) 8 and 16.
Using a harmonised panel of 30 countries (1980–2024), the authors construct a rare-event predictive framework combining logistic regression and ensemble learning under severe class imbalance. Model performance is evaluated using recall-sensitive metrics and policy-weighted loss functions. Shapley additive explanations-based explainability ensures regulatory transparency, and predicted probabilities are transformed into calibrated supervisory risk-alert bands.
While ensemble algorithms achieve high overall accuracy, they exhibit weak minority-class detection. Logistic regression demonstrates superior crisis recall (approximately 0.71), highlighting the importance of recall-oriented optimisation in supervisory contexts. Governance quality, inflation volatility, credit expansion and labour-market fragility emerge as dominant systemic risk drivers.
To the best of the authors’ knowledge, this study proposes the first SDG-aligned, explainable AI–RegTech crisis prediction architecture explicitly designed for supervisory deployment. By reframing crisis surveillance as a multidimensional governance challenge, the framework bridges predictive analytics, RegTech and sustainable development monitoring.
