A “pay-how-you-drive” car insurance approach through cluster analysis
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As discussed in the recent literature, several innovative car insurance concepts are proposed in order to gain advantages both for insurance companies and for drivers. In this context, the “pay-how-you-drive” paradigm is emerging, but it is not thoroughly discussed and much less implemented. In this paper, we propose an approach in order to identify the driver behavior exploring the usage of unsupervised machine learning techniques. A real-world case study is performed to evaluate the effectiveness of the proposed solution. Furthermore, we discuss how the proposed model can be adopted as risk indicator for car insurance companies.
KeywordsInsurance Risk analysis OBD CAN Cluster analysis Machine learning
This work has been partially supported by H2020 EU-funded projects NeCS and C3ISP and EIT-Digital Project HII and PRIN “Governing Adaptive and Unplanned Systems of Systems” and the EU project CyberSure 734815.
Compliance with ethical standards
Conflict of interest
All authors declare that there is no conflict of interest
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
This article does not contain any studies with animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
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