大数据环境下保险行业UBI车险定价模型构建探索

Exploration on the construction of UBI auto insurance pricing model in the insurance industry under the big data environment

传统车险产品大多依靠车辆自身状况进行保费的厘定,而UBI车险产品可以结合车辆之外的数据信息进行精准保费定价。通过多元回归等模型对UBI车险定价模型进行分析,结果显示行驶习惯、行驶技术、车辆信息、周围环境4个因素对UBI车险定价的影响尤为显著。分析不同性别和对UBI车险不同了解度的人群对保险定价的认知程度可知,性别差异和了解度差异影响较为显著,女性和对保险更了解的人群对保险定价的认知度更高。对比传统保险,UBI车险的深入运用将有助于减少逆向选择。

Traditional auto insurance products mostly rely on the condition of the vehicle itself to determine the premium, while UBI auto insurance products can combine the data information outside the vehicle to accurately price the premium. This paper analyzes the UBI auto insurance pricing model through multiple regression models. The results show that driving habits, driving technology, vehicle information and surrounding environment have a particularly significant impact on the pricing of UBI auto insurance. Analysis of the awareness of insurance pricing among people of different genders and different understandings of UBI auto insurance shows that gender differences and differences in understanding have a more significant impact, and women and people who know more about insurance have higher awareness of insurance pricing. Compared with traditional insurance, the indepth application of UBI auto insurance will help reduce adverse selection.