Determinants of operational efficiency on the financial health of non-life insurance companies in South Africa

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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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    • 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
    • Conceptualization
      Omonike Ige-Gbadeyan
    • Data curation
      Omonike Ige-Gbadeyan
    • Formal Analysis
      Omonike Ige-Gbadeyan
    • Investigation
      Omonike Ige-Gbadeyan
    • Methodology
      Omonike Ige-Gbadeyan
    • Resources
      Omonike Ige-Gbadeyan
    • Software
      Omonike Ige-Gbadeyan
    • Validation
      Omonike Ige-Gbadeyan
    • Writing – original draft
      Omonike Ige-Gbadeyan
    • Writing – review & editing
      Omonike Ige-Gbadeyan, Matthys Johannes Swanepoel
    • Funding acquisition
      Matthys Johannes Swanepoel
    • Project administration
      Matthys Johannes Swanepoel
    • Supervision
      Matthys Johannes Swanepoel
    • Visualization
      Matthys Johannes Swanepoel