City Research Online

Abstract

Telematics data from usage-based motor insurance provide valuable information –including vehicle usage, attitude towards speeding, time and proportion of urban/non-urban driving –that can be used for rate making. Additional information on acceleration, braking and cornering can likewise be usefully employed to identify near-miss events, a concept taken from aviation that denotes a situation that may have resulted in an accident. We analyze near-miss events from a sample of driversin order to identify the risk factors associated with a higher risk of near-miss occurrence. Our empirical application witha pilot sample of real usage-based insurance data reveals that certain factors are associated with a higher expected number of near-miss events, but that the association differs depending on the type of near-miss. We conclude that night time driving is associated with a lower risk of cornering events, urban driving increases the risk of braking events and speeding is associated with acceleration events. These results are relevant for the insurance industry in order to implement dynamic risk monitoring through telematics, as well as preventive actions.

Publication Type:	Article
Additional Information:	This is an Accepted Manuscript of an article published by Taylor & Francis in North American Actuarial Journal on 30 Aug 2019, available online: https://doi.org/10.1080/10920277.2019.1627221
Publisher Keywords:	usage-based insurance, pay-how-you-drive, predictive models, acceleration, braking, speeding
Subjects:	H Social Sciences > HG Finance
Departments:	Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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