Generalized Linear Models in Vehicle Insurance
| Authors | |
|---|---|
| Year of publication | 2014 |
| Type | Article in Periodical |
| Magazine / Source | Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis |
| MU Faculty or unit | |
| Citation | |
| Web | http://acta.mendelu.cz/62/2/0383/ |
| Doi | http://dx.doi.org/10.11118/actaun201462020383 |
| Field | Management and administrative |
| Keywords | vehicle insurance; generalized linear model; poisson distribution; link function; analysis of deviance; Akaike information criterion |
| Description | Actuaries in insurance companies try to find the best model for an estimation of insurance premium. It depends on many risk factors, e.g. the car characteristics and the profile of the driver. In this paper, an analysis of the portfolio of vehicle insurance data using a generalized linear model (GLM) is performed. The main advantage of the approach presented in this article is that the GLMs are not limited by inflexible preconditions. Our aim is to predict the relation of annual claim frequency on given risk factors. Based on a large real-world sample of data from 57 410 vehicles, the present study proposed a classification analysis approach that addresses the selection of predictor variables. The models with different predictor variables are compared by analysis of deviance and Akaike information criterion (AIC). Based on this comparison, the model for the best estimate of annual claim frequency is chosen. All statistical calculations are computed in R environment, which contains stats package with the function for the estimation of parameters of GLM and the function for analysis of deviation. |