Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data

Abstract

Citation: Ayuso, M., Guillén, M. and Nielsen, J. P. (2018). Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data. Transportation, This is the accepted version of the paper.This version of the publication may differ from the final published version.Permanent repository link: http://openaccess.city.ac.uk/19160/ Link to published version: http://dx. AbstractWe show how data collected from a GPS device can be incorporated in motor insurance ratemaking. The calculation of premium rates based upon driver behaviour represents an opportunity for the insurance sector. Our approach is based on count data regression models for frequency, where exposure is driven by the distance travelled and additional parameters that capture characteristics of automobile usage and which may affect claiming behaviour. We propose implementing a classical frequency model that is updated with telemetrics information. We illustrate the method using real data from usage-based insurance policies. Results show that not only the distance travelled by the driver, but also driver habits, significantly influence the expected number of accidents and, hence, the cost of insurance coverage. This paper provides a methodology including a transition pricing transferring knowledge and experience that the company already had before the telematics data arrived to the new world including telematics information.

Multivariate credibility modelling for usage-based motor insurance pricing with behavioural data

“…The approach proposed in this paper provides the actuary with a powerful alternative to the inclusion of behavioural traits as additional features in supervised learning (e.g. Baecke & Bocca, 2017; Ayuso et al ., 2018; Verbelen et al ., 2018; Jin et al ., 2018) or the unsupervised classification of driving styles into a few categories that can then supplement traditional risk factors in supervised learning (e.g. Weidner et al ., 2016, 2017; Wüthrich, 2017; Gao et al ., 2018).…”

The Use of Telematics Devices to Improve Automobile Insurance Rates

“…distance driven and vehicle location) and their driving behaviour (excess speed and aggressiveness). This information can improve the insurance ratemaking process and also allows conclusions to be drawn about how to make driving safer (Ayuso, Guillen, & Nielsen, 2018, Lemaire, Park, & Wang, 2016, Paefgen, Staake, & Fleisch, 2014, Ferreira & Minikel, 2013, Paefgen, Staake, & Thiesse, 2013, Langford, Koppel, McCarthy, & Srinivasan, 2008, Sivak et al, 2007, Litman, 2005and Edlin, 2003. New automobile insurance products (known by the acronyms PAYD, pay-asyou-drive, or PHYD, pay-how-you-drive) necessitate the introduction of a GPS device in the insured vehicle to record and store relevant information about variables that change over time, including, for example, the number of kilometres driven per day by the insured, the percentage of kilometres driven above the speed limit, and the percentage of kilometres driven at night, among others.…”

“…Various studies have explored the potential of telematics when applied to risks of road accidents, beginning in 1968 with a preliminary analysis by Vickrey (1968). More recently, several papers have examined the impact of new technologies on road safety and how driving habits can be measured (Shafique & Hato, 2015, Xu et al, 2015, Ellison, Bliemer, & Greaves, 2015, Ayuso, Guillen, & Pérez-Marín, 2014, Underwood, 2013, Jun, Guensler, & Ogle, 2011, Elias, Toledo, & Shiftan, 2010and Ayuso, Guillen and Alcañiz, 2010, while others have focused specifically on mileage and new risk factors that might be included in the ratemaking process, see Ayuso, Guillen, and Nielsen (2018) for an extended review. Recently, it has been proven that including standard telematics variables significantly improves risk assessment of insureds, therefore insurers should be able to tailor their products to the customers' risk profile (Baecke & Bocca, 2017).…”

A Longitudinal Analysis of the Impact of Distance Driven on the Probability of Car Accidents

“…Ayuso et al (2014) analyze the effect of various covariates for the time before the first crash, and compare novice and experienced drivers. More recently, Ayuso et al (2019) propose to improve the traditional ratemaking methods by including information related to risk exposure and driving behavior of insured. Denuit et al (2019) use predictive rating with past telematics information in a credibility model.…”