Evaluation of generalized linear models for modelling claim frequencies in vehicle insurance
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This study aims at evaluating the generalised linear models used for modelling vehicle insurance claim frequency. Modelling claim frequency, in turn, helps in the pricing and estimation of premiums. In this paper, claim frequencies will be modelled with respect to other risk factors present in the vehicle insurance data. This study makes use of data present in the CASdatasets that can be downloaded as an R package. The specific data used is brvehins1e that contains 393,071 observations. I further went ahead to randomly select 10,000 observations for computational purposes. The four generalised linear models namely; Poisson, negative Binomial, zero inflated Poisson and zero inflated negative Binomial models were fitted to the data to evaluate how well they fit. Comparison of the models was done using the Akaike' s Information Criteria, Bayesian Schwartz Information _Criteria and as well as performing a Vuong Test. Significant variables ii1. the model were determined using the p-values. The negative binomial model was determined as the better model when compared to the Poisson model. The zero inflated negative Binomial model was also seen to provide a better fit compared to the zero inflated Poisson model.