Innovative motor insurance schemes: A review of current practices and emerging challenges

Tselentis, Dimitrios I.; Yannis, George

doi:10.1016/j.aap.2016.10.006

Cited by 115 publications

(80 citation statements)

References 23 publications

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“…As pointed out by Tselentis et al . (2017), a gradual global transition towards UB insurance can therefore be envisaged. Low-risk drivers (low-mileage, less risky drivers, etc.)…”

Section: Discussionmentioning

confidence: 99%

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

2019

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Pay-how-you-drive (PHYD) or usage-based (UB) systems for automobile insurance provide actuaries with behavioural risk factors, such as the time of the day, average speeds and other driving habits. These data are collected while the contract is in force with the help of telematic devices installed in the vehicle. They thus fall in the category of a posteriori information that becomes available after contract initiation. For this reason, they must be included in the actuarial pricing by means of credibility updating mechanisms instead of being incorporated in the score as ordinary a priori observable features. This paper proposes the use of multivariate mixed models to describe the joint dynamics of telematics data and claim frequencies. Future premiums, incorporating past experience can then be determined using the predictive distribution of claim characteristics given past history. This approach allows the actuary to deal with the variety of situations encountered in insurance practice, ranging from new drivers without telematics record to contracts with different seniority and drivers using their vehicle to different extent, generating varied volumes of telematics data.

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“…As pointed out by Tselentis et al . (2017), a gradual global transition towards UB insurance can therefore be envisaged. Low-risk drivers (low-mileage, less risky drivers, etc.)…”

Section: Discussionmentioning

confidence: 99%

“…(2013) and Tselentis et al . (2017) for reviews of current practices and emerging challenges in usage-based (UB) motor insurance pricing.…”

Section: Introductionmentioning

confidence: 99%

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

2019

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“…As a result, insurers are better able to tailor their products to the customers' risk profile. Tselentis et al (2017) review the existing literature on usage-based insurance (UBI) schemes and research gaps are identified, showing that there is a multiplicity and diversity of several research studies accumulated in modern literature examining the correlation between PAYD (based on driver's travel behavior and exposure) and pay how-you drive (PHYD, based on driving behavior) schemes and crash risk in order to determine accident risk.…”

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

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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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“…With the help of UBI schemes, drivers could receive performance feedback and understand factors that may stimulate or motivate them to behave aggressively. Some UBI schemes that have been implemented around the world are summarized in the work by Husnjak et al [8] and Tselentis et al [9]. e main contribution of this paper is to explore the relationship between driver risk and factors indicating vehicle's speed and driver's acceleration behavior from a real road driving study.…”

Section: Introductionmentioning

confidence: 99%

A Case Study in China to Determine Whether GPS Data and Derivative Indicator Can Be Used to Identify Risky Drivers

Tong

Yu-xi

et al. 2019

Journal of Advanced Transportation

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This paper presents an investigation of the relationship between driver risk and factors indicating vehicle’s speed and driver’s acceleration behavior. The main objective is to examine whether GPS data and derivative indicator can be used to identify risky drivers by means of factor analysis. In doing so, a real road driving experiment is conducted to collect data. Fifty drivers are asked to drive along a route which includes both rural highways and urban roads. The trajectories are recorded by GPS devices to calculate speed and derive acceleration measures. Driver’s behavior is also recorded by cameras and analyzed by another group of volunteers to determine whether the driver is risky or not. The drivers are then classified into five groups with different levels of risk based on the scores obtained through factor analysis. The results are verified by the volunteer's categorization and further evaluated by symbolic aggregate approximation. A binary logistic regression model is established ultimately for predicting high-risk drivers. The potential applications of this study include developing quantitative measures to identify risky drivers, especially for auto-insurance companies with usage-based insurance (UBI) applications, bus companies, and transport enterprises.

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Innovative motor insurance schemes: A review of current practices and emerging challenges

Cited by 115 publications

References 23 publications

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

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

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

A Case Study in China to Determine Whether GPS Data and Derivative Indicator Can Be Used to Identify Risky Drivers

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