The use of context-sensitive insurance telematics data in auto insurance rate making

Luen, Yu; Zhu, Xiaoyu; Hu, Xianbiao; Chiu, Yi Chang

doi:10.1016/j.tra.2018.04.013

Cited by 60 publications

(44 citation statements)

References 22 publications

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“…Yu-Luen Ma et al confirm that mileage, peak time travel as well as driving behavior such as braking or starting habits are highly correlated with accident rate. They also confirm that contextual driving factors such as speeding and relative speed are important risk factors (Ma et al, 2018). Another interesting study described a partnership between one of Italy's largest auto insurers (Unipol) and a systems integrator (Octo Telematics) that yielded a new and very profitable customer value proposition for the insurer based on customer's driving and risk profiles (Peppard et al, 2011).…”

Section: Theoretical Framework 21 Telematics Technologymentioning

confidence: 84%

Analyzing the Vehicle Insurance Ecosystem in Estonia using Actor-Network Theory

Sai

Naroznaja²

2018

ESJ

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This paper focuses on analyzing the vehicle insurance ecosystem in Estonia that integrated Actor Network Theory (ANT) as theoretical lens to investigate potential transformations in the said ecosystem and to gauge the influence of insurance-oriented telematics technology. The paper combined an interpretive approach applied to a case study method. The study offers insights which are useful to facilitate the alignment of insurance-oriented telematics technology and its practical implementation within social systems. This study is one of the first to examine insurance-oriented telematics technology as a socio-technical process in Estonia.

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Section: Theoretical Framework 21 Telematics Technologymentioning

confidence: 84%

Analyzing the Vehicle Insurance Ecosystem in Estonia using Actor-Network Theory

Sai

Naroznaja²

2018

ESJ

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“…Predictors extrapolated from telematics data integrate traditional statistical predictors, such as age and sex of the driver or vehicle engine power, in view of the possibility of finding strong correlations between past and future (Baecke and Bocca, 2017;Guillen et al, 2019a;Wu¨thrich, 2017). Unlike traditional statistical factors, signals based on telematics data are obtained directly from the behaviour of the insured, while classic statistical data only offers proxy variables with respect to the prediction of future events (Ayuso et al, 2016;Baecke and Bocca, 2017;Denuit et al, 2019;Gao et al, 2019;Guillen et al, 2019a;Ma et al, 2018). Taking this difference into account, some research (Verbelen, 2018Wu¨thrich, 2017: 1ff) has hypothesised that telematic predictors not only work better but could even replace statistical variables in the near future offering, among other things, an effective strategy to circumvent the European legislation which prohibits the use of gender as variable in the pricing of motor insurance policies as a discriminatory practice.…”

Section: Discrimination and Fairnessmentioning

confidence: 99%

From pool to profile: Social consequences of algorithmic prediction in insurance

Cevolini

Esposito

2020

Big Data & Society

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The use of algorithmic prediction in insurance is regarded as the beginning of a new era, because it promises to personalise insurance policies and premiums on the basis of individual behaviour and level of risk. The core idea is that the price of the policy would no longer refer to the calculated uncertainty of a pool of policyholders, with the consequence that everyone would have to pay only for her real exposure to risk. For insurance, however, uncertainty is not only a problem – shared uncertainty is a resource. The availability of individual risk information could undermine the principle of risk-pooling and risk-spreading on which insurance is based. The article examines this disruptive change first by exploring the possible consequences of the use of predictive algorithms to set insurance premiums. Will it endanger the principle of mutualisation of risks, producing new forms of discrimination and exclusion from coverage? In a second step, we analyse how the relationship between the insurer and the policyholder changes when the customer knows that the company has voluminous, and continuously updated, data about her real behaviour.

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“…We focus our analysis on the distance driven, yet other telematics variables could be of interest. In the study by Verbelen et al (2018), driving time (daytime vs. nighttime) is studied along with the type of roads, while Ma et al (2018) find that speed and acceleration affect the expected claim frequency. Ayuso et al (2014) analyze the effect of various covariates for the time before the first crash, and compare novice and experienced drivers.…”

Section: Introductionmentioning

confidence: 99%

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

Boucher

Turcotte

2020

Risks

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Using telematics data, we study the relationship between claim frequency and distance driven through different models by observing smooth functions. We used Generalized Additive Models (GAM) for a Poisson distribution, and Generalized Additive Models for Location, Scale, and Shape (GAMLSS) that we generalize for panel count data. To correctly observe the relationship between distance driven and claim frequency, we show that a Poisson distribution with fixed effects should be used because it removes residual heterogeneity that was incorrectly captured by previous models based on GAM and GAMLSS theory. We show that an approximately linear relationship between distance driven and claim frequency can be derived. We argue that this approach can be used to compute the premium surcharge for additional kilometers the insured wants to drive, or as the basis to construct Pay-as-you-drive (PAYD) insurance for self-service vehicles. All models are illustrated using data from a major Canadian insurance company.

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The use of context-sensitive insurance telematics data in auto insurance rate making

Cited by 60 publications

References 22 publications

Analyzing the Vehicle Insurance Ecosystem in Estonia using Actor-Network Theory

Analyzing the Vehicle Insurance Ecosystem in Estonia using Actor-Network Theory

From pool to profile: Social consequences of algorithmic prediction in insurance

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

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