
Determinants of Financial Distress in the Sri Lankan Insurance Industry: Evidence from the Grover Model
By: D. H. V. Nilanka and H. J. R. Buddhika
Abstract
This study examines the determinants of financial distress in the Sri Lankan insurance industry by applying the Grover Model, a predictive model rarely utilized in this context. Covering the period from 2016 to 2022, the analysis considers both macroeconomic and firm-specific financial variables, including inflation, exchange rates, financial leverage, investment income ratios, and the risk-based capital ratio. The study uses secondary data from all 29 licensed insurance companies in Sri Lanka, treating the entire industry as both population and sample. Data were analyzed using Stata software through descriptive statistics, correlation analysis, regression modelling, and coefficient analysis to assess the relationships between the selected variables and financial distress. The results indicate that the risk-based capital ratio is a significant positive determinant of financial distress, highlighting the importance of capital adequacy in maintaining financial stability. In contrast, inflation, exchange rates, investment income, and financial leverage showed no statistically significant effect. These findings emphasize the critical role of capital risk management in the long-term survival and stability of insurance firms. Accordingly, the study recommends that insurers adopt prudent capital management practices to mitigate operational risks and enhance financial resilience, in alignment with the predictive guidance offered by the Grover Model.
DOI: https://doi.org/10.4038/sljbf.v8i1.67 | Journal eISSN: 2961-5348
Language: English
Page range: 68 - 83
Published on: Oct 7, 2025
Published by: Department of Banking and Finance, Wayamba University of Sri Lanka
In partnership with: Paradigm Publishing Services
© 2025 D. H. V. Nilanka, H. J. R. Buddhika, published by Department of Banking and Finance, Wayamba University of Sri Lanka
This work is licensed under the Creative Commons Attribution 4.0 License.