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COST-SENSITIVE MULTI-CLASS ADABOOST FOR UNDERSTANDING DRIVING BEHAVIOR BASED ON TELEMATICS

Published online by Cambridge University Press:  31 August 2021

Banghee So ,
Jean-Philippe Boucher  and
Emiliano A. Valdez Open the ORCID record for Emiliano A. Valdez [Opens in a new window]
Banghee So
Affiliation:
Department of Mathematics, Towson University, 7800 York Rd, Towson, MD, 21252, USA, E-Mail: bso@towson.edu
Jean-Philippe Boucher
Affiliation:
Département de Mathématiques, Université du Québec à Montréal, 201 Avenue du Président-Kennedy, Montréal, Québec, H2X 3Y7, Canada, E-Mail: boucher.jean-philippe@uqam.ca
Emiliano A. Valdez*
Affiliation:
Department of Mathematics, University of Connecticut, 341 Mansfield Road, Storrs, CT, 06269-1009, USA, E-Mail: emiliano.valdez@uconn.edu
*
E-Mail: emiliano.valdez@uconn.edu
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Abstract

Using telematics technology, insurers are able to capture a wide range of data to better decode driver behavior, such as distance traveled and how drivers brake, accelerate, or make turns. Such additional information also helps insurers improve risk assessments for usage-based insurance, a recent industry innovation. In this article, we explore the integration of telematics information into a classification model to determine driver heterogeneity. For motor insurance during a policy year, we typically observe a large proportion of drivers with zero accidents, a lower proportion with exactly one accident, and a far lower proportion with two or more accidents. We here introduce a cost-sensitive multi-class adaptive boosting (AdaBoost) algorithm we call SAMME.C2 to handle such class imbalances. We calibrate the algorithm using empirical data collected from a telematics program in Canada and demonstrate an improved assessment of driving behavior using telematics compared with traditional risk variables. Using suitable performance metrics, we show that our algorithm outperforms other learning models designed to handle class imbalances.

Keywords

Vehicle telematicsusage-based insurancecost-sensitive learningAdaBoostSMOTESAMMESAMME.C2.C10C51C52
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 51 , Issue 3 , September 2021 , pp. 719 - 751
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The International Actuarial Association

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