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Modelling a Pay-As-You-Drive Insurance Pricing Structure Using a Generalized Linear Model: Case Study of a Company in Kiambu

机译:使用广义线性模型对按需付费的保险定价结构建模:Kiambu一家公司的案例研究

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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.
机译:当前的汽车保险年度固定定价效率低下,而且精算不准确,因为处于同一风险等级的驾车者支付的保费数额相同,而与不同车辆行驶的里程数无关。在本文中,提出了一种简单的替代方法,即“开车时付钱”,即驾车者只需支付车辆所行驶的里程数即可。主要目标是找到合适的概率分布,该概率分布将用于为总总索赔成本的每公里风险溢价建模。对Kiambu县的一家公司进行了案例研究。收集的数据由194个类别中的5个变量组成,因此总索赔成本为因变量。数据收集技术是通过人口普查进行的。发现最合适的模型是零膨胀负二项式模型。发现重要的因素是车辆的制造,年度里程和车辆的现值。除此之外,还发现里程与总索赔成本成正相关。

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