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Does Botswana’s economic growth drive insurance sector development or follows it? This study evaluates the empirical applicability of the supply-leading and demand-following hypotheses. It employs the Autoregressive Distributed Lag bounds testing approach and Granger causality analysis within a Vector Error Correction Model framework, to examines the significance of dynamic long-run, short-run and causal relationship between the Insurance sector development (total insurance penetration) and economic growth (Gross Domestic Product per capita) over the period 2000–2023. To reduce specification bias, the control variables used are gross fixed capital formation for physical capital accumulation, inflation for macroeconomic stability and trade openness for external trade exposure. The findings indicate a statistically significant long-run cointegrating relationship among the variables, with inflation exerting a highly significant negative effect on Gross Domestic Product per capita while total insurance penetration indicates a statistically insignificant direct long-run impact. In the short run, total insurance penetration exerts a robust and statistically significant negative effect on Gross Domestic Product per capita, validating the presence of a 'savings-substitution' effect and domestic capital leakage channel identified in earlier literature. The Pairwise Granger causality analysis yields a strong backing for the demand-following hypothesis at 5% significance level, and a weak evidence of a supply-leading effect at 10% level meaning the insurance market acts primarily as a passive responder to macroeconomic expansion rather than a primary growth locomotive. Policy recommendations stress the need for regulatory reforms to boost domestic insurance premiums reinvestment, stimulate structural product innovation, and deepen financial inclusion to unlock growth-insurance complementarities.

In small and emerging markets like Botswana, what drives the financial resilience of non-life insurers? This study takes a closer look at this question, evaluating audited financial statement data from 14 Non-Bank Financial Institutions Regulatory Authority (NBFIRA) licensed insurers across the year 2010–2022 period. The resulting unbalanced panel dataset of 127 firm-year observations in total is used to test ten potential determinants: profitability, underwriting risk, reinsurance dependence, firm size, leverage, liquidity, asset tangibility, premium growth, firm age, and ownership structure. Following diagnostic testing via the F-test, Breusch-Pagan LM test, and Hausman specification test, the Fixed Effects (FE) model was retained as the preferred estimator. As findings, underwriting risk, rather than weakening solvency, showed the strongest positive association with capital adequacy. The most credible explanation for this is regulatory, companies with heavier claims exposure appear to respond to supervisory pressure by holding more capital, not less. Reinsurance dependence, firm size, liquidity, and asset tangibility were also positively significant, highlighting the importance of risk-transfer mechanisms and effective liquidity management. On the other hand, profitability, leverage, growth, and firm age fell short of significance reflecting the structural constraints of a concentrated, domestically oriented market rather than any theoretical irrelevance of these factors. Risk management practices and firm-specific structural characteristics positively influence solvency adequacy among Botswana's non-life insurers instead of profitability or growth considerations. This research adds to the already existing limited empirical evidence of drivers of solvency margins in Botswana and the Sub-Saharan Africa providing useful insights for insurers, regulators, and policymakers seeking to strengthen risk-based supervision and promote the long-term stability of the insurance sector in Botswana.

The South African insurance industry faces pricing challenges due to increasing catastrophic events driven by climate change and socio-economic uncertainties. The purpose is to find the best practices for vehicle pricing in South Africa. Five leading predictive models: Generalised Linear Models, Random Forest, XGBoost, Gradient Boosting Regressor, and Artificial Neural Networks are compared for estimating motor insurance premiums under normal and optimised conditions. Furthermore, analyses of catastrophic risk distribution are conducted. Results indicate that machine learning techniques, particularly XGBoost, achieve superior predictive accuracy under optimised conditions, along with the lowest RMSE and MAE. Traditional models like GLM ensure interpretability and regulatory compliance but struggle with non-linear complexities. However, integrating GLM with XGBoost enhances predictive performance. XGBoost is superior and GLM highly complies with the regulations. Gauteng is susceptible to malicious damage, KwaZulu-Natal is mostly exposed to floods and storms, and Western Cape is more vulnerable to droughts and earthquakes. These insights highlight the necessity of adaptive, data-driven risk management tailored to regional vulnerabilities. This research contributes to advancements in insurance pricing methodologies, offering actionable insights for policy development, disaster preparedness, and resilience-building strategies to address South Africa’s evolving risk landscape.