The goal of this paper is to analyze the problem of modeling claim counts in insurance, which entails studying variations in their occurrence by determining the distribution that best fits the observed data. As is well known, the Poisson distribution can be used to model many cases of discontinuous variables in practice. However, there are numerous examples of discontinuous random variables that do not adapt to this theoretical range model. One of these is the frequency of adverse events in motor third-party liability insurance when some of the derived Poisson distributions are used. Poisson-Gamma (negative binomial) distributions, Poisson-Inverse Gaussian distributions, Poisson-LogNormal distributions, and so on may be more appropriate. Among the models derived from Poisson process elements, the Good risk/bad risk (good driver/ad driver) model is examined in this paper for the modeling of claim counts in automobile third-party liability insurance. In that regard, the most crucial aspects in the process of selecting the probability of claim numbers was studied on a selected sample from a Serbian insurance company, and it was discovered that appropriate sample analysis based on the study of the insured's previous experience was one of the key elements in determining adequate premium systems.
Author(s) Details
Belgrade Banking Academy, Union University, Belgrade, Serbia.
Ivana Domazet
Institute of Economic Sciences, Belgrade, Serbia.
Hasan Hanic
Belgrade Banking Academy, Union University, Belgrade, Serbia.
Milica Bugarčić
Belgrade Banking Academy, Union University, Belgrade, Serbia.
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