Driver risk classification in auto insurance: Using deep neural networks and in-vehicle CAN bus data

dc.contributor.authorBest-Ezeani, Winston en
dc.descriptionApplied project submitted to the Department of Computer Science and Information Systems, Ashesi University, in partial fulfillment of Bachelor of Science degree in Management Information Systems, May 2020
dc.description.abstractAmong the numerous ways auto insurance companies can contribute to safety on the roads whilst creating more value for themselves and customers is by effectively analyzing individual driver risk level. Every driver’s driving style is their fingerprint and when insurance companies can anticipate their style and the risk associated with it, they can create safer portfolios, reward good driving with lower premiums, customize customer offerings and penalize bad driving with higher premiums. A good driver risk assessment lies in identifying and analyzing behavioral patterns in driving. The challenge however with prevalent risk assessment methods in motor insurance are that they rely on non-precise data (age, occupation, address) and their assessment is merely descriptive, leaving little to no detail about the individual nature of the risk a driver might pose. In this project I design a web interface for users and use In-vehicle sensor data that identify driver behaviour patterns and a deep neural network with continuous learning capabilities to analyse and predict the driver’s risk based on the data from their vehicle.
dc.description.sponsorshipAshesi University
dc.subjectauto insuranceen
dc.subjectdriver risk analysis systemen
dc.subjectweb-based applicationen
dc.titleDriver risk classification in auto insurance: Using deep neural networks and in-vehicle CAN bus data
dc.typeApplied projecten
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