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This study examines the linear and non-linear effects of enterprise risk management (ERM) and corporate governance (CG) on insurers’ risk-taking behaviour.

The study employed panel data of 63 insurers from South Africa over the period 2015 and 2019. The study used the generalised method of moments (GMM) to determine the direct relationship, while the dynamic panel threshold technique was utilised to discover whether there is non-linearity in the relationship and the threshold level at which ERM and CG stimulate insurance risk-taking.

The result from the GMM elicits a positive relationship between ERM and risk-taking, implying that insurers with a robust ERM system are more likely to pursue higher risks. The empirical evidence also suggests that board size and board independence improve insurers’ risk-taking. Contrarily, gender diversity shows an inverse relationship with risk-taking. The dynamic panel threshold regression confirms non-linearities between ERM, CG and risk-taking. The empirical evidence indicates a U-shaped relationship between ERM and risk-taking, implying that a robust ERM system increases insurers’ risk-taking and vice-versa. Further, board size and independence reveal an inverted U-shaped relationship, suggesting that larger boards and a higher proportion of independent directors exhibit lower risk-taking. However, gender diversity presents a negative relationship, demonstrating a strong impact at higher threshold levels. This tells that the presence of females on the board reduces insurers’ risk-taking preferences.

Due to the risk-bearing nature of the insurance business, it is required that they ensure a robust ERM system for prudent risk-taking decisions. This demands strict adherence to ERM principles and allocating sufficient resources for effective implementation. Also, there is a need for strong CG structures that pay more attention to diversity when selecting board members due to their influence in ensuring improved risk-taking choices.

This study contributes to the existing literature by providing insights into the under-researched role of ERM and CG in insurers’ risk-taking behaviour. The study further extends the literature by providing evidence on the non-linearity and threshold levels at which ERM and CG influence insurers’ risk-taking choices. The findings are unique and contribute to the growing body of literature documenting the need for strong ERM and CG systems in insurance companies.

Insurance companies play a significant role in every economy; hence, it is essential to investigate and understand the factors that propel their profitability. Unlike previous studies that present a linear relationship, this study provides initial evidence by exploring the non-linear impacts of the determinants of profitability amongst life insurers in South Africa.

The study uses a panel dataset of 62 life insurers in South Africa, covering 2013–2019. The generalised method of moments and the dynamic panel threshold estimation technique were used to estimate the relationship.

The empirical results from the direct relationship reveal that investment income and solvency significantly predict life insurance companies' profitability. On the other hand, underwriting risk, reinsurance and size reduce profitability. Further, the dynamic panel threshold analysis confirms non-linearities in the relationships. The results show that insurance size, investment income and solvency promote profitability beyond a threshold level, implying a propelling effect on life insurers' profitability at higher levels. Below the threshold, these factors have an adverse effect. The study further points to underwriting risk, reinsurance and leverage having a reduced effect on life insurers' profitability when they fall above the threshold level.

The findings suggest that insurers interested in boosting their profit position must commit more resources to maintain their solvency and manage their assets and returns on investment. The study further recommends that effective control of underwriting risk is critical to the profitability of the life insurance industry.

The study contributes to the literature by providing first-time evidence on the determinants of life insurance companies' profitability by way of exploring threshold effects in South Africa.