Time and distance to first accident and driving patterns of young drivers with pay-as-you-drive insurance

Ayuso, Mercedes; Guillén, Montserrat; Pérez-Marín, Ana M.

doi:10.1016/j.aap.2014.08.017

Cited by 66 publications

(54 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using models involving both policy and telematics predictors, Ayuso et al . (, ) studied the travelled time and distance to the first accident by using Weibull regression models. Paefgen et al .…”

Section: Statistical Background and Related Modelling Literaturementioning

confidence: 99%

Unravelling the Predictive Power of Telematics Data in Car Insurance Pricing

Verbelen

Antonio

Claeskens

2018

Journal of the Royal Statistical Society Series C: Applied Statistics

102

View full text Add to dashboard Cite

Summary A data set from a Belgian telematics product aimed at young drivers is used to identify how car insurance premiums can be designed based on the telematics data collected by a black box installed in the vehicle. In traditional pricing models for car insurance, the premium depends on self‐reported rating variables (e.g. age and postal code) which capture characteristics of the policy(holder) and the insured vehicle and are often only indirectly related to the accident risk. Using telematics technology enables tailor‐made car insurance pricing based on the driving behaviour of the policyholder. We develop a statistical modelling approach using generalized additive models and compositional predictors to quantify and interpret the effect of telematics variables on the expected claim frequency. We find that such variables increase the predictive power and render the use of gender as a rating variable redundant.

show abstract

Section: Statistical Background and Related Modelling Literaturementioning

confidence: 99%

Unravelling the Predictive Power of Telematics Data in Car Insurance Pricing

Verbelen

Antonio

Claeskens

2018

Journal of the Royal Statistical Society Series C: Applied Statistics

102

View full text Add to dashboard Cite

show abstract

“…This product was mainly sold to young drivers for the purpose of installing a GPS that would contact the company and indicate the location of the car in case of an emergency. This data set has been extensively studied in Ayuso et al (2014) among others.…”

Section: Description Of the Databasementioning

confidence: 99%

“…Boucher et al (2013) showed that the inclusion of the exact distance traveled in a Poisson Generalized Linear Models (GLM) model is able to explain the relationship between annual distance and frequency of claims. Meanwhile, using a Weibull regression model, Ayuso et al (2014) modeled the time and the number of kilometers traveled before a first communication of a claim by young policyholders who have purchased PAYD insurance. These authors observed significant differences by gender, so that men have a riskier driving pattern compared to women.…”

Section: Potential New Research Fieldmentioning

confidence: 99%

Exposure as Duration and Distance in Telematics Motor Insurance Using Generalized Additive Models

Boucher

Côté

Guillén

2017

Risks

View full text Add to dashboard Cite

Abstract:In Pay-As-You-Drive (PAYD) automobile insurance, the premium is fixed based on the distance traveled, while in usage-based insurance (UBI) the driving patterns of the policyholder are also considered. In those schemes, drivers who drive more pay a higher premium compared to those with the same characteristics who drive only occasionally, because the former are more exposed to the risk of accident. In this paper, we analyze the simultaneous effect of the distance traveled and exposure time on the risk of accident by using Generalized Additive Models (GAM). We carry out an empirical application and show that the expected number of claims (1) stabilizes once a certain number of accumulated distance-driven is reached and (2) it is not proportional to the duration of the contract, which is in contradiction to insurance practice. Finally, we propose to use a rating system that takes into account simultaneously exposure time and distance traveled in the premium calculation. We think that this is the trend the automobile insurance market is going to follow with the eruption of telematics data.

show abstract

“…For example, [14] argue that PAYD helps the insurance industry to target risk classes more effectively. Additionally, [15] report that the driving patterns and accident rates of young drivers with a PAYD contract are heterogeneous, so they should not be included in homogeneous risk groups. Meanwhile, the advantages for customers are clear: they pay a lower premium if they drive fewer kilometers or drive more safely.…”

Section: Introductionmentioning

confidence: 99%

Telematics and Gender Discrimination: Some Usage-Based Evidence on Whether Men’s Risk of Accidents Differs from Women’s

Ayuso

Guillén

Pérez-Marín

2016

Risks

Self Cite

View full text Add to dashboard Cite

Pay-as-you-drive (PAYD), or usage-based automobile insurance (UBI), is a policy agreement tied to vehicle usage. In this paper we analyze the effect of the distance traveled on the risk of accidents among young drivers with a PAYD policy. We use regression models for survival data to estimate how long it takes them to have their first accident at fault during the coverage period. Our empirical application with real data is presented and shows that gender differences are mainly attributable to the intensity of use. Indeed, although gender has a significant effect in explaining the time to the first crash, this effect is no longer significant when the average distance traveled per day is introduced in the model. This suggests that gender differences in the risk of accidents are, to a large extent, attributable to the fact that men drive more often than women. Estimates of the time to the first accident for different driver risk types are presented. We conclude that no gender discrimination is necessary if telematics provides enough information on driving habits.

show abstract

Time and distance to first accident and driving patterns of young drivers with pay-as-you-drive insurance

Cited by 66 publications

References 13 publications

Unravelling the Predictive Power of Telematics Data in Car Insurance Pricing

Unravelling the Predictive Power of Telematics Data in Car Insurance Pricing

Exposure as Duration and Distance in Telematics Motor Insurance Using Generalized Additive Models

Telematics and Gender Discrimination: Some Usage-Based Evidence on Whether Men’s Risk of Accidents Differs from Women’s

Contact Info

Product

Resources

About