Determinants of operational efficiency on the financial health of non-life insurance companies in South Africa
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DOIhttp://dx.doi.org/10.21511/ins.14(1).2023.11
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Article InfoVolume 14 2023, Issue #1, pp. 121-135
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This study aimed to determine the effect of operational efficiency on financial health of non-life insurance companies in South Africa. Operational efficiency refers to an insurer’s ability to deliver its services while minimizing costs and maximizing profitability. A descriptive research design was used to achieve the objective of this study. The panel data from 2008–2019 used secondary data sourced from S&P Capital Q and Refinitiv Eikon, well-known databases with readily available data. The population of this study focuses on 32 non-life insurance companies with measurable markets of 57 domestic non-life insurance providers in South Africa. Data were analyzed using Fixed-effect regression, (Random-effect GLS regression, correlation, and the Hausman test. The result reveals that of all the variables, only premium growth correlates significantly (negative correlation) with financial health. This could be a result of a specific investment that resulted in a lower rate than that of a risk-free security. It is also important to note that a negative premium does not always indicate a problem. This can happen due to cancellations of reinsurance, reinsurer closures, paid off reinsurance ahead of time, under- pricing policies, inadequate reserves, high claim frequency, operational inefficiencies, investment losses, inadequate risk assessment, economic downturn, regulatory changes, catastrophic event, and any other events. It is essential for non-life insurance companies to carefully manage their underwriting practices, risk assessment, pricing strategies, and investment portfolios to avoid negative premium situations and maintain financial health.
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JEL Classification (Paper profile tab)M10, C10, G22
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References92
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Tables5
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Figures0
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- Table 1. Descriptive characteristics of variables
- Table 2. Correlation analysis
- Table 3. Regression analysis fixed effect (within data)
- Table 4. Regression analysis random effect
- Table 5. Hausman test
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