Modelling of Auto Insurance Claims Using Discrete Probability Distributions

Abstract

Within general insurance, pricing of premiums is always a challenging task. Frequency of claims plays a big part in pricing of premiums. Frequency of claims are determined by the attributes of a particular policy holder. Count regression analysis allows one to nd out which characteristic of a policy holder plays a signi cant role in determining the frequency of claim and also in predicting the frequency of claims given the characteristics of a particular policy holder. The objective of this thesis is to nd out which among the Poisson, NB1 and NB2 models is a better t to the count data under consideration. The count data is from Kenendia insurance. The best model is chosen based on the log-likelihood method and the Akaike’s Information Criteria (AIC).