Collective loss reserving with two types of claims in motor third party liability insurance

This paper adopts the new loss reserving approach proposed by Denuit and Trufin (2016),inspired from the collective model of risk theory. But instead of considering the whole setof claims as a collective, two types of claims are distinguished, those claims with relativelyshort development patterns and claims requiring longer developments. In each case, thetotal payment per cell is modelled by means of a Compound Poisson distribution withappropriate assumptions about the severities. A case study based on a motor third partyliability insurance portfolio observed over 2004–2014 is used to illustrate the approachproposed in this paper. Comparisons with Chain-Ladder are performed and reveal significantdifferences in best estimates as well as in Value-at-Risk at high probability levels.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Collective Loss Reserving with Two Types of Claims in Motor Third Party Liability Insurance

This paper adopts the new loss reserving approach proposed by Denuit and Trufin (2016), inspired from the collective model of risk theory. But instead of considering the whole set of claims as a collective, two types of claims are distinguished, those claims with relatively short development patterns and claims requiring longer developments. In each case, the total payment per cell is modelled by means of a Compound Poisson distribution with appropri- ate assumptions about the severities. A case study based on a motor third party liability insurance portfolio observed over 2004-2014 is used to illustrate the approach proposed in this paper. Comparisons with Chain-Ladder are performed and reveal significant differences in best estimates as well as in Value-at-Risk at high probability levels

Predictive Power of Criminal Background on Losses

Product and data science teams for the auto insurance industry have been trying to increase pricing segmentation with validated rating variables to decrease rate subsidization. The criminal background data availability provided a new behavior variable to test against insurance-based credit scores as a potential predictive variable in the generalized linear rating model. Criminal background was analyzed using a Poisson Log Linear model and other key insurance rating variables for predicting loss costs. The study supported the inclusion of the criminal background data in combination with insurance-based credit score as the variable’s addition could improve the overall fit of the predictive model. The study also acknowledged there was a statistically significant association between criminal background and insurance-based credit score, but the overall size of the effect was small and weak. The overall contribution of value criminal background variable needs to be considered with a full rating dataset to determine if other, less powerful variables could be removed from the generalized linear to reduce the overall model complexity