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Bu çal??man?n amac?, Türkiye’de hayat d??? sigortac?l?k sektöründe aktif karl?l??? ile firmaya özgü faktörler aras?ndaki ili?kiyi tespit ederek aktif kârl?l???n?n etkinlikten mi yoksa yo?unla?madan m? kaynakland???n? belirlemektir. Bu amaçla, Türk sigorta sektöründe faaliyet gösteren 36 hayat d??? sigorta ?irketi aras?nda 2015 y?l? verilerine göre pazar?n toplam %83.38’ini olu?turan 15 sigorta ?irketinin 2010-2015 dönemine ait mali verileri ile performans ili?kisine yönelik Period SUR panel veri analizi yap?lm??t?r. Analiz sonuçlar?na göre, aktif büyüklü?ü, likidite ve pazar pay? de?i?kenleri ile aktif karl?l??? aras?nda pozitif ve istatistiki olarak anlaml? bir ili?ki tespit edilmi?ken kald?raç, hasar/prim oran? ve firma ya?? de?i?kenleri aras?nda ise negatif ve istatistiki olarak anlaml? bir ili?ki tespit edilmi?tir.  Analiz dönemi boyunca Herfindahl–Hirschman Endeksi de?erleri 1.500’ün alt?nda oldu?u için Türkiye’de hayat d??? sigortac?l?k sektörünün yo?un olmayan bir piyasa görünümünde oldu?u tespit edilmi?tir. Bu nedenle, hayat d??? sigortac?l?k sektöründe aktif kârl?l??? yo?unla?madan daha ziyade faaliyetlerdeki etkinlikten kaynaklanmaktad?r.

This paper aims to investigate the relationship between profitability measured with ROE along with EPS and several industry-specific factors such as underwriting risk, reinsurance dependence, solvency margin, leverage, liquidity risk, premium growth, size of the company, tangibility of assets as well as macroeconomic factors such as GDP growth, inflation, and stock market development of the general insurance companies of Bangladesh based on the sample of 7 insurance companies for the period of 2010-2019. For the analyses, several econometric models have been adopted to estimate the coefficients such as Pooled OLS, Cross-sectional GLS, Fixed-effect, Random-effect, and One-step GMM approaches. Moreover, diagnostic tests have also been conducted to examine the validity of the models such as model specification tests, heteroskedasticity tests, multicollinearity tests, autocorrelation tests, and unit-root tests. According to the computed coefficients, none of the macroeconomic variables utilized in the model affects profitability, except inflation, which positively affects ROE. Underwriting risk and size are found to negatively affect ROE but positively affect EPS. Of the two measures of profitability, only the EPS is affected by premium growth. Except for Fixed-effect, reinsurance dependence has a large positive influence on EPS and ROE. In contrast, liquidity and leverage have a positive impact on ROE. The tangibility of assets negatively affects both EPS and ROE where EPS is significant and ROE is insignificant only under fixed effect. Aside from premium growth, underwriting risk and solvency margin have a positive impact on EPS. Finally, using the GMM technique, we find that no lag variables are significant, indicating no profitability persistence. However, the model fits the data well for predicting EPS and ROE as profitability measures explaining 89.38 percent of EPS variation and 80.38 percent of ROE variation using Pooled OLS.

The pricing of insurance premiums in the non-life insurance sector remains a challenging and complex task. It demands a delicate balance between accurately estimating risk exposure and ensuring profitability for insurers. Generalised Linear Regression Models (GLMs) have become the preferred methods for premium price modelling in the motor insurance sector. While the approach of using a single superior model on which predictions are based ignores the use of robust estimator models. This paper examines various methodologies and sheds light on superiority of twenty-two models compared to each other for pricing automobile insurance. These methods vary from traditional actuarial methods to the modern statistical models such as machine learning algorithms. By using archival technique, their inferiority and superiority are explored, considering the ever-changing landscape of risk factors and market dynamics. Furthermore, it highlights the potential benefits of leveraging these methods and the mechanism for pricing short-term insurance, particularly in motor vehicle insurance. It also develops a framework that can be used in pricing to cater to risk analysis constituents to mitigate uncertainties and provide good services to clients. Our findings show that ANN, NN, XGB, random forest (RF) are superior models, and we conclude that the modern statistical methods can accurately estimate the risk exposure as compared to traditional methods such as the GLMs.

The financial market in general is where supply and demand for funds are met. In other words, funds saved in the financial market are directed to investments in order to contribute to the economy. In the Macedonian financial market, the banking sector is really dominant but also pension funds and insurance funds have an active role. This research will be studied the distribution of pension funds’ portfolios in the North Macedonian financial market. The global financial crisis in 2008 negatively affected the economies and financial markets of many countries, including the USA and Europe, and the effects of the crisis continued until 2013. This negatively affected the confidence in the financial markets, and the current surplus of funds was directed towards low-risk but fixed or low-yield financial instruments. As of 31.12.2018, fund accumulation in private pension funds amounted to 1.04 billion euros, corresponding to approximately 10 percent of GDP. The total premium production of insurance companies is around 157 million euros, while the GDP ratio of premium production is around 1.5 percent. Funds collected in both sectors are generally used in treasury bills, bank deposits, and stocks traded on the Macedonia Stock Exchange and foreign financial markets.

This research analyzed the insurance industry and economic growth in Nigeria between 1980 and 2015. Secondary data ranging from real gross domestic product, the premium of the insurance business, claim expenditure of insurance industry and inflation rate were utilized and sourced from Central Bank of Nigeria (CBN). The Ordinary Least Squares (OLS) multiple regression techniques, Descriptive statistics, Augmented Dickey-Fuller (ADF) test of stationarity and ARDL Bound Test Co-Integration were adopted for the model in the Study. The findings revealed that the premium of the insurance industry (PMI) impacts negatively on economic growth both in the extended and short run period. The claim expenditure of the insurance industry (CEX) revealed a progressive relationship with economic growth in the long run and a negative relation with growth in the short run. We therefore conclude following the Keynesian theory of aggregate demand which states that, ‘’if we will have to wait till the long run, we will all be dead’’, that insurance industry in Nigeria has not impacted positively on economic growth of Nigeria within the period under review and its operations and investment have not been prominent and contributory to the growth and development of Nigeria. Based on these outcomes, we recommend amongst others, that more investment ought to be made towards the insurance industry in Nigeria especially in terms of proper policy formulation by the government that would channel funds and encourage competition among the players in the industry and invariably contribute to the growth of the economy.

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.

This study examines the obstacles to the adoption of Old Mutual Protect Covers in South Africa through a quantitative methodology.  The study seeks to ascertain critical variables—namely income level, education level, and faith in insurance companies—that affect the probability of acquiring certain insurance products.  Data was gathered via a structured survey of 500 South African people and analysed utilising SPSS version 29.0, focussing on Pearson correlation and regression analysis.  The results indicate substantial favourable correlations among income level, education level, and faith in insurance firms with the use of Old Mutual Protect Covers.  These findings indicate that socio-economic considerations and customer confidence are pivotal in the acquisition of insurance.  The report continues with recommendations for Old Mutual to improve awareness and trust among lower-income and less-educated demographics to boost the adoption of their Protect Covers.  The study's limitations and recommendations for future research are also addressed.

The objective of this study was to evaluate how measures taken to prevent fraud impact the Profitability of insurance companies operating in South Africa. The study adopted a descriptive research approach and surveyed a sample of 70 licensed insurance companies in the country. Primary sources of data were obtained by engaging claims managers in each of the firms, and descriptive and inferential statistics were used to analyse the data. The results of the study revealed that fraud mitigation measures significantly affect the Profitability of insurance companies in South Africa. The implementation of fraud prevention, detection, and response measures had a positive impact on the Profitability of these firms. Based on the findings, the study recommends that the Insurance Regulatory Authority establish regulations that mandate all insurance providers to collect and report statistics on fraud. Insurance firms should provide their employees with comprehensive training on fraud management and implement strict penalties and disciplinary measures for employees involved in fraudulent activities, as they can undermine the effectiveness of the fraud mitigation process.