Abstract

Predictive modeling is a key technique in auto insurance rate-making and the decision-making involved in the review of rate filings. Unlike an approach based on hypothesis testing, the results from predictive modeling not only serve as statistical evidence for decision-making, they also discover relationships between a response variable and predictors. In this work, we study the use of predictive modeling in auto insurance rate filings. This is a typical area of actuarial practice involving decision-making using industry loss data. The aim of this study was to offer some general guidelines for using predictive modeling in regulating insurance rates. Our study demonstrates that predictive modeling techniques based on generalized linear models (GLMs) are suitable in auto insurance rate filings review. The GLM relativities of major risk factors can serve as the benchmark of the same risk factors considered in auto insurance pricing.

Highlights

  • We present the results of a comparative study employing minimum bias procedure (MBP) and generalized linear models (GLMs) as modeling tools

  • We considered an industry-level data set for accident years from 2007–2009, with third party liability (TPL) coverage and urban territory

  • The relativities of Driving Record (DR) and Class at various levels were obtained for the accident year 2007 data set by both MBP and GLM

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Summary

Introduction

Modeling aggregate loss (Duan 2018; Frees 2014; Meyers 2007; Shi 2016) using insurance risk factors is a key aspect in the decision-making of rate change review application. In David (2015), the Poisson regression and negative binomial models were applied to a French auto insurance portfolio to investigate the degree of risk using claim counts data. The analysis of car insurance data from the SAS Enterprise Miner database was used to show the usefulness of the proposed method in rate-making. All those works focused on the study of the loss experience of an individual company, rather than on the total loss at the industry level. A study of total loss behavior at the industry level becomes important in providing a constructive review of a company’s rate change applications

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