Guillen Montserrat, Nielsen Jens Perch, Ayuso Mercedes, Pérez-Marín Ana M
Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, Barcelona, Spain.
Cass Business School, City, University of London, London, UK.
Risk Anal. 2019 Mar;39(3):662-672. doi: 10.1111/risa.13172. Epub 2018 Dec 19.
Most automobile insurance databases contain a large number of policyholders with zero claims. This high frequency of zeros may reflect the fact that some insureds make little use of their vehicle, or that they do not wish to make a claim for small accidents in order to avoid an increase in their premium, but it might also be because of good driving. We analyze information on exposure to risk and driving habits using telematics data from a pay-as-you-drive sample of insureds. We include distance traveled per year as part of an offset in a zero-inflated Poisson model to predict the excess of zeros. We show the existence of a learning effect for large values of distance traveled, so that longer driving should result in higher premiums, but there should be a discount for drivers who accumulate longer distances over time due to the increased proportion of zero claims. We confirm that speed limit violations and driving in urban areas increase the expected number of accident claims. We discuss how telematics information can be used to design better insurance and to improve traffic safety.
大多数汽车保险数据库包含大量零索赔的投保人。这种高频率的零索赔可能反映出一些被保险人很少使用他们的车辆，或者他们不想为小事故提出索赔以避免保费增加，但也可能是因为驾驶技术好。我们使用来自按驾驶付费的被保险人样本的远程信息处理数据来分析风险暴露和驾驶习惯的信息。我们将每年行驶的距离作为零膨胀泊松模型中偏移量的一部分，以预测零索赔的超额情况。我们表明，对于行驶距离的大值存在学习效应，因此驾驶时间越长，保费应该越高，但由于零索赔比例增加，随着时间积累行驶距离更长的驾驶员应该有折扣。我们证实，违反速度限制和在城市地区驾驶会增加事故索赔的预期数量。我们讨论了如何利用远程信息处理信息来设计更好的保险并提高交通安全。
