Exploring Industry-Level Fairness of Auto Insurance Premiums by Statistical Modeling of Automobile Rate and Classification Data

Shengkun Xie (), Rebecca Luo and Yuanshun Li
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Shengkun Xie: Global Management Studies, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Rebecca Luo: Global Management Studies, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Yuanshun Li: School of Accounting and Finance, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada

Risks, 2022, vol. 10, issue 10, 1-21

Abstract: The study of actuarial fairness in auto insurance has been an important issue in the decision making of rate regulation. Risk classification and estimating risk relativities through statistical modeling become essential to help achieve fairness in premium rates. However, because of minor adjustments to risk relativities allowed by regulation rules, the rates charged eventually may not align with the empirical risk relativities calculated from insurance loss data. Therefore, investigating the relationship between the premium rates and loss costs at different risk factor levels becomes important for studying insurance fairness, particularly from rate regulation perspectives. This work applies statistical models to rate and classification data from the automobile statistical plan to investigate the disparities between insurance premiums and loss costs. The focus is on major risk factors used in the rate regulation, as our goal is to address fairness at the industry level. Various statistical models have been constructed to validate the suitableness of the proposed methods that determine a fixed effect. The fixed effect caused by the disparity of loss cost and premium rates is estimated by those statistical models. Using Canadian data, we found that there are no significant excessive premiums charged at the industry level, but the disparity between loss cost and premiums is high for urban drivers at the industry level. This study will help better understand the extent of auto insurance fairness at the industry level across different insured groups characterized by risk factor levels. The proposed fixed-effect models can also reveal the overall average loss ratio, which can tell us the fairness at the industry level when compared to loss ratios by the regulation rules.

Keywords: insurance risk classification; fairness; rate regulation; generalized linear models; fixed effect models (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
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