Connected Cars and Driving Pattern: An Analytical Approach to Risk-Based Insurance

SrinivasaRao Valluru ()
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SrinivasaRao Valluru: HCL Technologies

A chapter in Advances in Analytics and Applications, 2019, pp 131-137 from Springer

Abstract: Abstract Insurance companies are witnessing a significant drop in their profit margins particularly in the segment of vehicle insurance due to heavy competition in the industry. Insurance companies are trying to improve their customer base by retaining existing customers and launching new policies with additional benefits. Customers are expecting insurance policies which match to their requirements and at the same time, companies also want to charge more premium for the customers with risky driving behaviour and less for safe driving. Insurance companies are reducing costs with the help of historical risk data and advanced analytics to improve their profits. Insurance companies are capturing real-time vehicle movement data through IoT to monitor the driving behaviour of their customers. By applying advanced analytics on this data, insurance companies can study customers driving pattern to assess the risk involved in it. In this study, we are presenting an analytical approach to categorize driving patterns using advanced machine learning techniques which will lead to risk-based insurance premium. It will help insurance companies to provide personalized services to their customers and in assisting insurance companies in the process of claims approval when an accident took place.

Keywords: K-means clustering; Decision tree; Fuzzy forest; Random forest (RF); Support vector machine (SVM) (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-981-13-1208-3_12

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DOI: 10.1007/978-981-13-1208-3_12

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