Understanding driver’s behavior with telematics for usage-based insurance

Conférenciers: Emiliano Valdez et Banghee So, de l'Université du Connecticut

Résumé / Abstract: The evolving and rapid development of technology is revolutionizing the auto assurance market. Powered with telematics technology, insurers are able to capture a wide range of data, such as distance traveled, how drivers brake and make turns and frequency of travel during each day of the week, to better decode driver's behavior. Such additional information helped insurers introduce an innovative product called usage-based insurance (UBI). UBI has long been in the market, but in this presentation, we discuss the advantages and drawbacks of UBI to both insurers and the policyholders, and explore how we can integrate telematics information to better predict the frequency and cost of claims. We use empirical data collected from the telematics program in Canada and did exploratory data analysis to investigate how the information can be used to assess the frequency of claims. To comparably understand the improvement in predictions using traditional and telematics risk variables, we calibrated various classification models: logistic regression, penalized logistic regression (LASSO) and classification trees with random forests. For training, we use a sampled data drawn from year 2015 with 25829 observations; for testing, we use a samples data drawn from the year 2016 with 24419 observations. We find that broadly speaking, the additional information derived form vehicle telematics help refine risk classifications of drivers of UBI. 

This is joint work with Jean-Philippe Boucher from the Université du Québec à Montréal (UQAM) 

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vendredi 12 avril 2019
14 h à 16 h

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UQAM - Pavillon Président-Kennedy (PK)
PK-2205
201, avenue du Président-Kennedy
Montréal (QC)

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Gratuit

inscription obligatoire à diaz.marie-soleil@uqam.ca

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