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    The value of vehicle telematics data in insurance risk selection processes

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    Publication type
    Vlerick strategic journal article
    Author
    Baecke, Philippe
    Bocca, L.
    Publication Year
    2017
    Journal
    Decision Support Systems
    Publication Volume
    98
    Publication Begin page
    69
    Publication End page
    79
    
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    Abstract
    The advent of the Internet of Things enables companies to collect an increasing amount of sensor generated data which creates plenty of new business opportunities. This study investigates how this sensor data can improve the risk selection process in an insurance company. More specifically, several risk assessment models based on three different data mining techniques are augmented with driving behaviour data collected from In-Vehicle Data Recorders. This study proves that including standard telematics variables significantly improves the risk assessment of customers. As a result, insurers will be better able to tailor their products to the customers' risk profile. Moreover, this research illustrates the importance of including industry knowledge, combined with data expertise, in the variable creation process. Especially when a regulator forces the use of easily interpretable data mining techniques, expert-based telematics variables are able to improve the risk assessment model in addition to the standard telematics variables. Further, the results suggest that if a manager wants to implement Usage-Based-Insurances, Pay-As-You-Drive related variables are most valuable to tailor the premium to the risk. Finally, the study illustrates that this new type of telematics-based insurance product can quickly be implemented since three months of data is already sufficient to obtain the best risk estimations.
    Keyword
    Insurance, Marketing
    Knowledge Domain/Industry
    Marketing & Sales
    Special Industries : Financial Services Management
    DOI
    10.1016/j.dss.2017.04.009
    URI
    http://hdl.handle.net/20.500.12127/5772
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.dss.2017.04.009
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