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

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

doi:10.3390/risks4020010

Cited by 64 publications

(50 citation statements)

References 12 publications

(11 reference statements)

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“…The same authors (Ayuso et al 2016a) conclude that the observed differences in gender are largely attributable to the intensity of vehicle usage: while gender has a significant effect in explaining the time elapsed until the first accident, it has no longer effect when the average daily distance traveled is introduced into the model. Therefore, the authors conclude that discrimination based on gender is not necessary if telematics provide enough information on driving patterns (see Ayuso et al 2016b). This same conclusion is also obtained by Verbelen et al (2017) using a Belgian database.…”

Section: Potential New Research Fieldsupporting

confidence: 65%

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

Boucher

Côté

Guillén

2017

Risks

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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.

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Section: Potential New Research Fieldsupporting

confidence: 65%

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

Boucher

Côté

Guillén

2017

Risks

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“…Vehicle power presents a positive effect in the traditional model as well in the model that includes all variables, but this is not the case with gender, which is not significant when we include the telematics variables. Indeed, Ayuso et al (2016b) stress the importance of including the new variables of risk exposure and driver behaviour in the new framework that prohibits companies from charging different premiums according to the gender of the driver. Finally, the results are the same for the model with telematics variables and the version with offsets (columns 3 and 4), with a significant influence of the annual distance but also with the percentage of kilometres driven per year over the speed limit and the percentage of urban kilometres driven per year.…”

Section: Resultsmentioning

confidence: 99%

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

2018

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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.

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“…This study is interested in predicting Y using the aforementioned covariates. This data set has been extensively studied in Ayuso et al (2014Ayuso et al ( , 2016aAyuso et al ( , 2016b and Boucher et al (2017). Table 1 shows the descriptive statistics for the accident claims data set.…”

Section: Data and Descriptive Statisticsmentioning

confidence: 99%

Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression

Pesantez-Narvaez

Guillén

Alcañiz

2019

Risks

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108

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XGBoost is recognized as an algorithm with exceptional predictive capacity. Models for a binary response indicating the existence of accident claims versus no claims can be used to identify the determinants of traffic accidents. This study compared the relative performances of logistic regression and XGBoost approaches for predicting the existence of accident claims using telematics data. The dataset contained information from an insurance company about the individuals' driving patterns-including total annual distance driven and percentage of total distance driven in urban areas. Our findings showed that logistic regression is a suitable model given its interpretability and good predictive capacity. XGBoost requires numerous model-tuning procedures to match the predictive performance of the logistic regression model and greater effort as regards to interpretation.Risks 2019, 7, 70 2 of 16 among many others. Here, pay-as-you-drive (PAYD) insurance schemes represent an alternative method for pricing premiums based on personal mileage travelled and driving behaviors. Guillen et al. (2019), Verbelen et al. (2018, and Pérez-Marín and Guillén (2019) showed the potential benefits of analyzing telematics information when calculating motor insurance premiums. Gao and Wüthrich (2019) analyzed high-frequency GPS location data (second per second) of individual car drivers and trips. Gao and Wüthrich (2018) and investigated the predictive power of covariates extracted from telematics car driving data using the speed-acceleration heatmaps proposed by Wüthrich (2017). Further, Hultkrantz et al. (2012) highlighted the importance of PAYD insurance plans insofar as they allow insurance companies to personalize premium calculation and, so, charge fairer rates.The rest of this paper is organized as follows. First, the notation is introduced and the logistic regression and XGBoost methods are outlined. Second, our dataset is described and some descriptive statistics are provided. Third, the results of our comparisons in both a training and a testing sample are reported. Finally, following the conclusion, some practical suggestions are offered about the feasibility of applying new machine learning methods to the field of insurance.

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

Cited by 64 publications

References 12 publications

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

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

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

Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression

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