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All Journal Research of Finance and Banking
Majumder, Benazir Imam
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Economics, Econometrics & Finance

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Predicting Non-Life Insurers’ Financial Distress: Evidence from Bangladesh Palas, Md. Jahir Uddin; Majumder, Benazir Imam
Research of Finance and Banking Vol. 3 No. 2 (2025): October 2025
Publisher : SAN Scientific

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58777/rfb.v3i2.546

Abstract

This study aims to develop an interpretable early-warning framework to predict financial distress among non-life insurers in Bangladesh. The research addresses the question of which financial and operational indicators most accurately signal early signs of distress in the insurance sector. Using a firm-year panel covering 2014–2024, the study applies penalized logistic regression, random forests, and gradient-boosted trees, combined with class-balancing remedies and SHAP-based interpretability techniques, to identify the key determinants of insurer distress. The results show that gradient-boosted trees achieve the highest out-of-time recall performance. At the same time, SHAP analysis consistently identifies the management expense ratio, lagged underwriting performance, and reinsurance intensity as the most influential predictors. Robust tests across alternative sampling and feature reduction methods confirm the stability of these findings. The study concludes that monitoring expense efficiency, underwriting results, and reinsurance practices provides the most reliable early warning signals of financial distress in thin-premium markets. The originality of this research lies in integrating explainable machine learning with operational financial indicators in a developing-market insurance context, producing a transparent and policy-ready predictive model that balances accuracy and interpretability.
Interpretable Machine Learning for Predicting Financial Distress in Emerging Market Insurance Sectors Palas, Jahir Uddin; Majumder, Benazir Imam
Research of Finance and Banking Vol. 4 No. 1 (2026): APRIL 2026
Publisher : SAN Scientific

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58777/rfb.v4i1.548

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.
Co-Authors Palas, Jahir Uddin Palas, Md. Jahir Uddin