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UBI products focus on identifying the relationship between the individual driver\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.1155\/2021\/6784026","type":"journal-article","created":{"date-parts":[[2021,6,12]],"date-time":"2021-06-12T18:50:14Z","timestamp":1623523814000},"page":"1-10","source":"Crossref","is-referenced-by-count":4,"title":["Individual Driver Crash Risk Classification Based on IoV Data and Offline Consumer Behavior Data"],"prefix":"10.1155","volume":"2021","author":[{"given":"Xuemei","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China"},{"name":"Shandong Management University, Jinan, Shandong 250357, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5478-3678","authenticated-orcid":true,"given":"Ting","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonghui","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Management, Shanghai University of International Business and Economics, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/s1369-8478(01)00029-8"},{"key":"2","volume-title":"Global Status Report on Road Safety 2018","author":"WHO","year":"2018"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1111\/j.1539-6975.2009.01337.x"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.7819\/rbgn.v17i57.1741"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2014.2300753"},{"key":"6","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1016\/j.future.2013.01.010","article-title":"Internet of Things (IoT): a vision, architectural elements, and future directions","volume":"29","author":"G. 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