Interpretable Machine Learning for Predicting Financial Distress in Emerging Market Insurance Sectors

Abstract

This study develops an interpretable early-warning framework to predict financial distress among non-life insurers operating in a thin-premium market context. While prior studies widely rely on traditional models such as the Z-score, a critical research gap remains, as these models are not well-suited to the insurance industry due to its unique capital structures, regulatory requirements, and underwriting dynamics. Specifically, conventional distress prediction approaches tend to overlook operational characteristics such as reinsurance dependency, reserve adequacy, and expense management, which are central to insurer solvency. Addressing this gap, the study applies machine learning techniques combined with explainable artificial intelligence to enhance both predictive capability and transparency. Using firm-level panel data, the research incorporates key financial and operational indicators to construct a context-specific predictive framework. The methodology emphasizes balanced model evaluation, feature relevance, and interpretability to ensure practical applicability for supervisory authorities. By integrating explainability into predictive modeling, this study helps bridge the regulatory trust gap associated with black-box algorithms. The proposed framework offers a policy-relevant tool for early identification of vulnerable insurers and for facilitating timely intervention. Overall, this research advances the literature by aligning predictive accuracy with institutional usability in emerging insurance markets.