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The value of vehicle telematics data in insurance risk selection processes
Baecke, Philippe Bocca, L.
Baecke, Philippe
Bocca, L.
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Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2017
Journal
Decision Support Systems
Book
Publication Volume
98
Publication Issue
Publication Begin page
69
Publication End page
79
Publication Number of pages
Collections
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.
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Keywords
Insurance, Marketing