With the development of big data technologies, usage-based insurance (UBI) has received considerable attention from insurance companies. UBI products focus on identifying the relationship between the individual driver’s risk and online channel behavior variables from Internet of Vehicles (IoV) data. Although omnichannel information integration has promoted the development of many industries, it has not been used to improve the accuracy of driver risk classification models in insurance industries. This paper investigates the role of combining different channel variables in improving the classification of driver’s risk. Specifically, several models, including logistic regression and three different data mining techniques (neural networks, random forests, and support vector machines), augmented with driving behavior data based on the IoV and offline consumer behavior data collected from 4S (Sale, Spare part, Service, Survey) dealers, are applied to the classification model of risk. The empirical results show that the inclusion of online and offline channel data improves the different risk assessments; results also demonstrate the importance of offline consumer behavior variables in different models. These insights have important implications for insurance companies on UBI pricing strategy and cost management.

中文翻译：

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基于车联网数据和线下消费者行为数据的个人驾驶员碰撞风险分类

随着大数据技术的发展，基于使用的保险（UBI）受到了保险公司的广泛关注。UBI 产品侧重于从车联网 (IoV) 数据中识别个体驾驶员风险与在线渠道行为变量之间的关系。尽管全渠道信息整合促进了许多行业的发展，但并未用于提高保险行业驾驶员风险分类模型的准确性。本文研究了结合不同渠道变量在改进驾驶员风险分类中的作用。具体来说，几种模型，包括逻辑回归和三种不同的数据挖掘技术（神经网络、随机森林和支持向量机），以车联网为基础的驾驶行为数据和从4S（Sale、Sare part、Service、Survey）经销商处收集的线下消费者行为数据，应用于风险分类模型。实证结果表明，线上线下渠道数据的纳入提高了不同风险评估；结果还证明了线下消费者行为变量在不同模型中的重要性。这些见解对保险公司在 UBI 定价策略和成本管理方面具有重要意义。结果还证明了线下消费者行为变量在不同模型中的重要性。这些见解对保险公司在 UBI 定价策略和成本管理方面具有重要意义。结果还证明了线下消费者行为变量在不同模型中的重要性。这些见解对保险公司在 UBI 定价策略和成本管理方面具有重要意义。