Innovative Insurance Schemes: Pay as/how You Drive

Tselentis, Dimitrios I.; Yannis, George; Vlahogianni, Eleni I.

doi:10.1016/j.trpro.2016.05.088

Cited by 74 publications

(46 citation statements)

References 23 publications

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“…Closer aligning insurance policies to the actual risks increases actuarial fairness and reduces cross-subsidization compared with grouping drivers into too general actuarial classes (Desyllas and Sako, 2013). Telematics insurance gives a high incentive to change the current driving pattern and stimulates more responsible driving (Parry, 2005;Litman, 2015;Tselentis et al, 2016). Users' feedback on driving behaviour and game design elements in UBI can further enhance the customer experience by making it more interactive, gratifying and even exciting (Toledo et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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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

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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: Introductionmentioning

confidence: 99%

“…Usage-based insurance (Tselentis et al, 2016) includes pay as you drive and pay how you drive schemes. The pay as you drive scheme focuses on the driving habits, e.g.…”

Section: Introductionmentioning

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

“…Recent studies have proved that customer satisfaction is more in the case of good drivers who participate in UBI programs [9]. UBI programs reward the best drivers and help insurance companies not only to classify and price them appropriately but also to engage with them to build better relationships.…”

Section: Related Workmentioning

confidence: 99%

A survey on driving behavior analysis in usage based insurance using big data

Arumugam

Bhargavi

2019

J Big Data

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An accident is defined as an unfortunate incident that happens unexpectedly and unintentionally, typically resulting in damage or injury. Considering all the consequences that could eventuate after an accident, there are reasons to believe that a normal person does not drive with an ex-ante intention to cause an accident. Holding a valid driving license is prerequisite to drive in any part of the world and during the licensing

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“…Closer aligning insurance policies to the actual risks increases actuarial fairness and reduces cross-subsidization compared to grouping the drivers into too general actuarial classes (Desyllas and Sako, 2013). In addition, some positive externalities are to be expected (Parry, 2005;Litman, 2015;Tselentis et al, 2016). Telematics insurance gives a high incentive to change the current driving pattern and stimulates more responsible driving.…”

Section: Introductionmentioning

confidence: 99%

“…Usage-based insurance includes Pay-as-you-drive (PAYD) and pay-how-you-drive (PHYD) schemes (Tselentis et al, 2016). PAYD focuses on the driving habits, e.g.…”

Section: Introductionmentioning

confidence: 99%

Unraveling the Predictive Power of Telematics Data in Car Insurance Pricing

2016

View full text Add to dashboard Cite

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, 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 behavior of the policyholder. We develop a statistical modeling 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 discriminating rating variable redundant.

show abstract

Innovative Insurance Schemes: Pay as/how You Drive

Cited by 74 publications

References 23 publications

Unravelling the Predictive Power of Telematics Data in Car Insurance Pricing

Unravelling the Predictive Power of Telematics Data in Car Insurance Pricing

A survey on driving behavior analysis in usage based insurance using big data

Unraveling the Predictive Power of Telematics Data in Car Insurance Pricing

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