Modelling a Pay-As-You-Drive Insurance Pricing Structure Using a Generalized Linear Model: Case Study of a Company in Kiambu
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 6, November 2015, Pages: 527-533
Received: Sep. 28, 2015;
Accepted: Oct. 15, 2015;
Published: Oct. 30, 2015
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Charity Mkajuma Wamwea, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Benjamin Kyalo Muema, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Joseph Kyalo Mung’atu, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
The current fixed car-year pricing of auto insurance is inefficient and actuarially inaccurate since motorists in the same risk class pay the same amount of premium regardless of the number of miles covered by the different vehicles. In this paper, a simple alternative, the pay as you drive insurance, was proposed whereby motorists only pay for the mileage covered by their vehicles. The main objective was to find a suitable probability distribution that would be used to model the per kilometer risk premiums for the total aggregate claims cost. A case study was done for a company in Kiambu county. The data collected consisted of 5 variables in 194 categories whereby the total aggregate claims cost was the dependent variable. The data collection technique was via a census. The most appropriate model was found to be the zero inflated negative binomial model. The significant factors were found to be the make of the vehicle, annual mileage, and present value of the vehicle. In addition to this, mileage was also found to be positively correlated to the total aggregate claims cost.
Charity Mkajuma Wamwea,
Benjamin Kyalo Muema,
Joseph Kyalo Mung’atu,
Modelling a Pay-As-You-Drive Insurance Pricing Structure Using a Generalized Linear Model: Case Study of a Company in Kiambu, American Journal of Theoretical and Applied Statistics.
Vol. 4, No. 6,
2015, pp. 527-533.
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