The Cost of Legal Restrictions on Experience Rating

Abstract

We investigate the cost of legal restrictions on experience rating in auto and home insurance. The cost is an opportunity cost as experience rating can mitigate the problems associated with unobserved heterogeneity in claim risk, including mispriced coverage and resulting demand distortions. We assess this cost through a counterfactual analysis in which we explore how risk predictions, premiums, and demand in home insurance and two lines of auto insurance would respond to unrestricted multiline experience rating. Using claims data from a large sample of households, we first estimate the variance‐covariance matrix of unobserved heterogeneity in claim risk. We then show that conditioning on claims experience leads to material refinements of predicted claim rates. Last, we assess how households’ demand for coverage would respond to multiline experience rating. We find that the demand response would be large.

Predictive Claim Scores for Dynamic Multi-Product Risk Classification in Insurance

“…Vector GLMs, multivariate integer-valued autoregressive processes, and multivariate decision trees, on the other hand, can allow for this correlation by directly describing the vector of claims of a customer (see, e.g., Yee and Hastie, 2003;Bermúdez et al, 2018;Quan and Valdez, 2018). Multivariate random effects and multivariate credibility can additionally enable a dynamic correction of the a priori rate structure by absorbing any variation not already accounted for by the covariates in GLMs (see, e.g., Englund et al, 2008Englund et al, , 2009Pechon et al, 2018;Barseghyan et al, 2020). However, Lemaire (1998) argues that past claiming behavior is one of the most important determinants of future claim counts and that a bonus-malus system (BMS) is therefore more intuitive for this correction.…”

Predictive Claim Scores for Dynamic Multi-Product Risk Classification in Insurance

“…Common shocks, copulas and Vector GLMs, for instance, can induce a correlation structure between claims from different product categories (see, e.g., Yee and Hastie (2003); Bermúdez and Karlis (2011); Shi and Valdez (2014)). Multivariate random effects and multivariate credibility can additionally accommodate for a dynamic correction of the a priori rate structure by absorbing any variation not already accounted for by the covariates in GLMs (see, e.g., Englund et al (2008Englund et al ( , 2009; Barseghyan et al (2018); Pechon et al (2018)). However, Lemaire (1998) argues that past claiming behavior is one of the most important determinants of future claim counts and that a Bonus-Malus System (BMS) is therefore more intuitive for this correction.…”