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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.

An investigation to balance the sustainability of insurance companies and successful claims on insurance policies is important towards the economy, as well as for assisting the insured during financial difficulty when their items are damaged or lost. This paper examines some rejected short-term insurances where a misunderstanding of the contract might have caused the insurer to lose out on the claim. Data were collected from cases of courts and insurers on rejected claims due to misunderstandings by the insured. The first text data set was a court case. The second was a graph on the root causes of claims rejections from the insurer. The third was a matrix data set presenting the languages of the insured. The insurers who had refused to pay insurer claims supplied the latter two. Despite the three contexts being widely different, their results show that rejections of claims by the insurers were due to the insured failing to understand the conditions of the contracts, and thus not being able to figure out what could have caused the rejection. Hence, the use of complicated and legal language in insurance contracts results in the insured not fully understanding the contract, which leads to claims being rejected. The paper recommends the use of simple language and pinpointing in layman's form, those aspects that could lead to rejections of claims.

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.