Abstract
This paper will discuss the modeling of claim frequency from Indonesian auto insurance using the generalized Poisson-Lindley linear model. This modeling method assumes that the data of claim frequency are from populations that follow generalized Poisson-Lindley distribution. Generalized Poisson-Lindley linear model is an alternative to modeling count data that contains overdispersion. The parameters in the generalized Poisson-Lindley linear model can be estimated using the maximum likelihood estimation method through Newton Raphson's iteration numerical method. The data are the secondary data took from XYZ Company for the 2013 policy which is overdispersed. The data contains policyholder partial loss claims for comprehensive motor vehicle insurance products. From the research conducted it was concluded that the data is suitable to be modeled with generalized Poisson-Lindley linear models and produce better models than ordinary Poisson linear modeling because of produced the smaller AIC value. Of the 3 predictor variables that are modeled on the frequency of claims, 2 variables influenced they are the use variable and vehicle brand variable.
Highlights
This paper will discuss the modeling of claim frequency from Indonesian auto insurance using the generalized Poisson-Lindley linear model
This modeling method assumes that the data of claim frequency are from populations
Generalized Poisson-Lindley linear model is an alternative to modeling count data that contains overdispersion
Summary
429 Mardianto Karim, Aceng Komarudin Mutaqin pemegang polis [15]. Salah satu produk asuransi yang ada di Indonesia yaitu asuransi kendaraan bermotor. Pemodelan frekuensi klaim yang telah dilakukan dalam kasus asuransi kendaraan bermotor seperti pemodelan frekuensi klaim menggunakan distribusi Gomez-Deniz Et. Al [5], dan pemodelan frekuensi klaim yang dilakukan menggunakan generalized linier model dengan asumsi data frekuensi klaim berdistribusi binomial negatif [3]. Salah satu metode pemodelan yang fleksibel digunakan untuk data overdispersi yaitu generalized Poisson-Lindley linear model [16]. Dalam penelitian yang dilakukan sebelumnya [16], disimpulkan bahwa pemodelan generalized Poisson-Lindley linear model memiliki tingkat kecocokan paling tinggi dalam memodelkan data overdispersi dibandingkan dengan pemodelan menggunakan model Poisson, model binomial negatif maupun model Poisson-weighted eksponensial. Berdasarkan penelitian Wongrin dan Bodhisuwan [16], dalam makalah ini peneliti melakukan pemodelan terhadap data frekuensi klaim asuransi kendaraan bermotor di Indonesia menggunakan generalized Poisson-Lindley linear model
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