Cost-Sensitive Multi-Class Adaboost for Understanding Driving Behavior Based on Telematics

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.

Weather Conditions and Telematics Panel Data in Monthly Motor Insurance Claim Frequency Models

“…The literature presents evidence of the association between other telematics variables and the risk of having an accident. There is evidence of a better assessment of driving behaviour when detailed telematics information is used compared with traditional risk factors (see, for example, So et al 2021). Specifically, speed, road type, and the time of day when the vehicle is used seem to have a strong relationship with the number of claims, and alternative models have been proposed in the literature to describe the joint dynamics of telematics data and claim occurrence (Ayuso et al 2014;Guillen et al 2019;Corradin et al 2022, among others).…”

Microscopic traffic models, accidents, and insurance losses

“…We refer to Gao et al (2022) for a methodological overview. Verbelen et al (2018), Corradin et al (2021), So et al (2021), and Henckaerts and Antonio (2022) discuss telematics pricing and usage-based auto insurance products.…”

“…(2021), So et al . (2021), and Henckaerts and Antonio (2022) discuss telematics pricing and usage-based auto insurance products.…”

Enhancing claim classification with feature extraction from anomaly‐detection‐derived routine and peculiarity profiles

“…Ayuso et al (2019) propose a methodology in which driving habits‐related risk factors are used as a correction to the premium calculated with traditional risk factors (TRFs). Other studies, in conjunction with driving habits, investigate the predictive power of driving style‐related data (Gao et al, 2018, 2019, 2021; Gao & Wüthrich, 2019; Huang & Meng, 2019; So et al, 2021; Wüthrich, 2017). Guillen et al (2020, 2021) develop a pricing scheme in which near‐miss events, situations in which an accident is “narrowly” averted, and which comprise harsh braking, harsh acceleration and smartphone usage events, are used to update a baseline premium on a weekly basis.…”