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

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