Abstract
The standard Poisson distribution is widely used as a mechanism for regression modeling of count data outcomes. However, the suitability of this modeling technique is only limited to equi-dispersed count data outcomes. This is due to the fact that this modeling technique does not take into account the problems associated with over dispersion and excess zeros in many data sets as with insurance claims data. The study objective is to model domestic package insurance claims frequency using zero inflated and hurdle models since insurance portfolios are characterized by the non-occurrence of claims over a given time interval. This non-occurrence of claims over a given time interval usually leads to the Zero-Inflation and Dispersion associated with insurance claims data. The study consequently evaluates the performance of the Poisson, Zero Inflated Poisson (ZIP) and Hurdle Poisson (HP) models in determining the model that best models the domestic package insurance claims data. This is then used to estimate, predict and determine the heterogeneity of occurrence of the aforementioned insurance claims. The statistical Hosmer-Lemeshow tests is used to define the suitability of the fitted model to estimate the zero-inflation and over-dispersion characteristic of the data. To determine the presence of outliers and the distribution of residuals, the Residual Pearson and Deviance statistics are used. Data on a number of claims for domestic package insurance policy from Madison Insurance ltd, Kenya spanning from 2014 to 2018 (261 weeks) is used in the study.
Highlights
The insurance product is distinctive in nature in that its quality can only be judged when something goes wrong
This study concerns itself with modeling claim frequency modeling as it is an indispensable component for premium determination, a vital yet a difficult undertaking, by the insurers in insurance industry
This was achieved by use of the classical statistical Poisson regression model by which different rating factors were justified by the use of a regression coefficient
Summary
The insurance product is distinctive in nature in that its quality can only be judged when something goes wrong For this reason, the way in which a claim is handled has important market repercussions for an insurer. This study concerns itself with modeling claim frequency (number of claims) modeling as it is an indispensable component for premium determination, a vital yet a difficult undertaking, by the insurers in insurance industry. This was achieved by use of the classical statistical Poisson regression model by which different rating factors were justified by the use of a regression coefficient. This methodology proved not to provide accurate results since insurance claims count data possess a specific characteristic of having an excess number of zeros for a International Journal of Data Science and Analysis 2020; 6(5): 137-144 particular time interval which is not catered for by the classical Poisson regression coefficients
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
