Predictive AI Models For Sovereign Debt Risk In Emerging Economies: A Machine Learning Framework For Early Warning And Default Prediction
DOI:
https://doi.org/10.64771/ijaiems.v1.i2.08Keywords:
Sovereign Debt Risk, Machine Learning, Emerging Economies, Early Warning Systems, Gradient Boosting, LSTM, SHAP, Fiscal SustainabilityAbstract
Sovereign debt crises within emerging economies continue to be one of the most destabilising forces in the global financial system, but as illustrated, current risk assessment practices – led by agency-produced credit ratings and linear econometric models – have sadly not guarded against pre-crisis warnings. This study creates and validates Sovereign Debt AI Prediction Framework (SDAIPF), an integrated machine learning architecture that combines gradient-boosted ensemble methods, Long-short-term memory (LSTM) neural networks and eXplainable Artificial Intelligence (XAI) to forecast sovereign default risk for a panel of 48 emerging economies between the years 2000-2023. The SDAIPF is measured using macroeconomic, fiscal, external-sector, institutional quality and financial market indicators from World Bank, IMF and BIS databases with the out-of-sample area under the receiver operator characteristic curve (AUC-ROC) of 0.921 and F1-score of 0.884, significantly better than traditional logit models (AUC-ROC = 0.741) and sovereign credit ratings. Near-term default events are mainly driven by external debt-to-GDP ratio, real effective exchange rate volatility, fiscal primary balance and institutional governance quality, as shown by SHAP (SHapley Additive exPlanations) analysis. The framework exhibits good generalisability across regions and with a good early-warning period of 14-month average crystalisation in advance. This research makes several key contributions to the field of debt distress that are relevant in emerging markets: First, developing an early warning system of the type of direct policy utility of finance ministries, international financial institutions and sovereign bond investors, which is appropriate and understandable in emerging market environments.
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Copyright (c) 2026 Rajesh Shahi (Author)
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