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

This study analyses how insurers categorise risk to price car insurance, focusing on fairness and potential bias in pricing models. Using a large semi-realistic dataset, the author applies statistical tests (ANOVA and t-tests) to assess how age, gender, race, driving experience and credit score correlate with claim behaviour. Results show that age, gender, driving experience and credit score are significantly associated with claim behaviour, while race is not: younger, less experienced, male drivers with lower credit scores file claims more often. The paper also compares compulsory and optional coverage frameworks in the United Kingdom, China and the United States. It argues for more accurate, data-driven models to improve fairness and accessibility for higher-risk demographic groups.