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Modeling data count is an important thing in various fields. For this purpose, Poisson regression models are often used. However, in this model there is an assumption of equidispersion data where the mean value equals the value of the variance. In fact, this assumption is often violated in the observed data. In real data, the value of variance actually exceeds the mean (overly dispersed) value with the cause of the overdispersion depending on many situations. When the overdispersion source is exceeds zero (excess zero), then a more suitable model to use is the Zero-inflated Poisson regression model. In this paper, after the framework of Poisson regression and the Zero-inflated Poisson regression is reviewed then the model is adjusted to the claim frequency data in a private health insurance scheme where the frequency of claims is overly dispersed because of the number of zeros in the data set. Then Vuong’s test is done to compare the two models and obtain the result that the Zero-inflated Poisson regression is more suitable for modeling the frequency data of PT.XYZ Health Insurance claims.

Zero-Inflated Poisson Regression Analysis On Frequency Of Health Insurance Claim PT. XYZ