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Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data

机译：使用远程学习改善汽车保险：包含里程和驾驶员行为数据

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We show how data collected from a GPS device can be incorporated in motor insurance ratemaking. The calculation of premium rates based upon driver behaviour represents an opportunity for the insurance sector. Our approach is based on count data regression models for frequency, where exposure is driven by the distance travelled and additional parameters that capture characteristics of automobile usage and which may affect claiming behaviour. We propose implementing a classical frequency model that is updated with telemetrics information. We illustrate the method using real data from usage-based insurance policies. Results show that not only the distance travelled by the driver, but also driver habits, significantly influence the expected number of accidents and, hence, the cost of insurance coverage. This paper provides a methodology including a transition pricing transferring knowledge and experience that the company already had before the telematics data arrived to the new world including telematics information.
机译：我们展示了如何在汽车保险中收集来自GPS设备的数据。基于驾驶员行为的高级率的计算代表了保险部门的机会。我们的方法基于计数数据回归模型，用于频率，其中曝光由距离行驶的距离和捕获汽车使用特征的额外参数，并且可能影响声称的行为。我们建议实现与遥测信息更新的经典频率模型。我们说明了使用基于使用的保险策略的真实数据的方法。结果表明，不仅驾驶员行驶的距离，而且驾驶习惯，显着影响了预期的事故次数，因此，保险范围的成本。本文提供了一种方法，包括过渡定价，在远程信息处理数据到达新世界之前已经拥有的传输知识和经验，包括远程信息。

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