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Estimating Major Risk Factor Relativities in Rate Filings Using Generalized Linear Models

Shengkun Xie () and Anna T. Lawniczak ()
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Shengkun Xie: Ted Rogers School of Management, Ryerson University, Toronto, ON M5B 2K3, Canada
Anna T. Lawniczak: Mathematics and Statistics, University of Guelph, Guelph, ON N1G 2W1, Canada

International Journal of Financial Studies, 2018, vol. 6, issue 4, 1-14

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.

Keywords: rate Filings; auto insurance regulation; generalized linear models; rate-making; predictive modeling (search for similar items in EconPapers)
JEL-codes: G1 G2 G3 F2 F3 F41 F42 (search for similar items in EconPapers)
Date: 2018
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