The Applications of Generalized Poisson Regression Models to Insurance Claim Data

Pouya Faroughi (), Shu Li and Jiandong Ren
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Pouya Faroughi: School of Mathematical and Computational Sciences, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada
Shu Li: Department of Statistical and Actuarial Sciences, Western University, London, ON N6A 5B7, Canada
Jiandong Ren: Department of Statistical and Actuarial Sciences, Western University, London, ON N6A 5B7, Canada

Risks, 2023, vol. 11, issue 12, 1-17

Abstract: Predictive modeling has been widely used for insurance rate making. In this paper, we focus on insurance claim count data and address their common issues with more flexible modeling techniques. In particular, we study the zero-inflated and hurdle-generalized Poisson and negative binomial distributions in a functional form for modeling insurance claim count data. It is shown that these models are useful in addressing the problem of excess zeros and over-dispersion of the claim count variable. In addition, we show that including the exposure as a covariate in both the zero and the count part of the model is an effective approach to incorporating exposure information in zero-inflated and hurdle models. We illustrate the effectiveness and versatility of the introduced models using three real datasets. The results suggest their promising applications in insurance risk classification and beyond.

Keywords: risk classification; count data; over-dispersion; hurdle-generalized Poisson regression; hurdle negative binomial regression; exposure; shrinkage (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2023
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