Car Insurance: An Analysis of Risk Factors, Claim Behaviour and Global Car Insurance Frameworks | RISE Research

This paper analyses the risk factors and claim behaviour underlying car insurance and compares global insurance frameworks, with a central concern for fairness and discrimination in how insurers price risk. Because insurers categorise risk through actuarial principles and statistical generalisation, the study asks whether common rating variables produce accurate pricing or unjust outcomes for particular groups. It examines insurance in the United Kingdom, China and the United States, distinguishing compulsory from optional coverage such as third-party liability and comprehensive cover. Methodologically, the paper uses a large semi-realistic dataset containing risk variables including age, gender, race, driving experience and credit score, and applies statistical tests such as ANOVA and t-tests to relate these factors to claim behaviour. It finds that age, gender, driving experience and credit score are associated with claim behaviour, while race is not relevant; younger, less experienced, male drivers with lower credit scores show higher rates of claims. The paper situates these results within actuarial concepts such as risk pooling, the law of large numbers, and the distinction between correlation and causation, and weighs the dual nature of insurance as both a protective financial mechanism and a source of affordability barriers for high-risk groups. Its focus is on the tension between actuarial accuracy and equity: how generalisation across risk brackets, even when statistically defensible, can raise concerns of bias and discrimination. Spanning actuarial science, statistics, risk management and public policy, the paper argues for accurate, data-focused and fairer models that improve accessibility across a wider demographic rather than entrenching disadvantage.