The Contribution of Usage-Based Data Analytics to Benchmark Semi-autonomous Vehicle Insurance

Guillén, Montserrat; Pérez-Marín, Ana M.

doi:10.1007/978-3-319-89824-7_75

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…However, the reason to introduce a multivariate framework is that, for instance a claim in 2009 can influence the driver in such a way that he or she drives more carefully in 2010 in terms of excess speed and even in the total distance. This phenomenon had been noted before (see Guillen and Pérez-Marín, 2018) but it had not been studied in the way it is done here.…”

Section: Case Studymentioning

confidence: 66%

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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Section: Case Studymentioning

confidence: 66%

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

2019

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“…On the positive side, vehicle accidents will be significantly reduced, but on the negative side, the identification of accident responsibility will become more complex, and the subject of responsibility for accidents will no longer be just the driver, but the original equipment manufacturers (OEMs) and their developers as well. In recent years, some scholars have discussed the identification methods of accident liability for semi-autonomous and autonomous vehicles [124], and some scholars have also studied the driving risk quantification methods for them [125,126]. However, only literature [126] mentioned the UBI product form of an autonomous vehicle.…”

Section: Research On Differentiated Macro Driving Stylementioning

confidence: 99%

“…In recent years, some scholars have discussed the identification methods of accident liability for semi-autonomous and autonomous vehicles [124], and some scholars have also studied the driving risk quantification methods for them [125,126]. However, only literature [126] mentioned the UBI product form of an autonomous vehicle. It can be seen that in the era of autonomous vehicles, there are still many thorny issues required to be further studied, including the definition of responsibility subject, product form design, and the coexistence mode of manual driving and autonomous vehicle UBI products, etc.…”

Section: Research On Differentiated Macro Driving Stylementioning

confidence: 99%

A Comprehensive Review of Driving Style Evaluation Approaches and Product Designs Applied to Vehicle Usage-Based Insurance

Nai¹,

Yang²,

Wei³

et al. 2022

Sustainability

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Vehicle insurance is a very important source of income for insurance companies, and it is closely related to the driving style performed by driving behavior. Different driving styles can better reflect the driving risk than the number of violations, claims, and other static statistic data. Subdivide the vehicle insurance market according to the personal characteristics and driving habits of the insured vehicles, and studying the personalized vehicle insurance products, will help the insurance companies to improve their income, help the drivers to change their bad driving habits, and thus help to realize the healthy development of the vehicle insurance industry. In the past 20 to 30 years, more and more insurance companies around the world have launched vehicle usage-based insurance (UBI) products based on driving style analysis. However, up to now, there are few comprehensive reports on commercial vehicle UBI products and their core driving risk assessment methods. On the basis of literature indexing on the Web of Science and other academic platforms by using the keywords involved in vehicle UBI, over 100 relevant works of literature were screened in this paper, and a detailed and comprehensive discussion on the driving style evaluation methods and the design of commercial vehicle UBI products during the past 20 to 30 years has been made, hoping to get a full understanding of the possible factors affecting driving style and the collectible data that can reflect these factors, and to get a full grasp of the developing status, challenges and future trends in vehicle insurance branch of the Internet of Vehicles (IoV) industry.

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“…The academic research on the motor insurance business often deals with the highly competitive market conditions, market growth, and market profitability (e.g., Eling & Luhnen, 2008, 2010; Maichel‐Guggemoos & Wagner, 2018). Attention has also been given to the consequences of individual major mobility “subtrends” or product designs with a focus on usage‐based insurance (e.g., Guillen & Perez‐Marin, 2018; Kraft & Hering, 2017; Sheehan et al, 2017; Weidner, Vanella, & Zuchandke, 2015; Wu, 2019). For instance, Sheehan et al (2017) apply a Bayesian network to examine the potential consequences of autonomous driving for the motor insurance business and the changing risk landscape based on the five levels of automation defined by the Society of Automotive Engineers (SAE).…”

Section: Introductionmentioning

confidence: 99%

The future of mobility and its impact on the automobile insurance industry

Gatzert

Osterrieder

2020

Risk Manage Insurance Review

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Among the trends impacting most industries are new mobility concepts, digitalization, urbanization, rising environmental awareness, and demographic change. The automobile insurance industry, in particular, is strongly affected by new mobility concepts, including autonomous, shared, and electric vehicles, which are expected to increasingly impact the risk exposure and insurance demand in the future. Identifying and assessing the resulting risk and opportunity landscape from these trends thus becomes a major strategic challenge for insurers. The aim of this paper is to analyze the trends that impact the field of mobility and thus automobile insurers. Based on this, we derive a set of strategic response measures for insurers to enable them to be prepared for the future of mobility.

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The Contribution of Usage-Based Data Analytics to Benchmark Semi-autonomous Vehicle Insurance

Cited by 3 publications

References 8 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

A Comprehensive Review of Driving Style Evaluation Approaches and Product Designs Applied to Vehicle Usage-Based Insurance

The future of mobility and its impact on the automobile insurance industry

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