The effects of feedback and incentive-based insurance on driving behaviours: study approach and protocols

Stevenson, Mark; Harris, Anthony; Mortimer, Duncan; Wijnands, Jasper S.; Tapp, Alan; Peppard, F; Buckis, Samantha

doi:10.1136/injuryprev-2016-042280

Cited by 17 publications

(15 citation statements)

References 29 publications

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“…These two findings are also similar with findings from other researchers (e.g. Müller and Te, 2017;Stevenson et al, 2017;Lesmana et al, 2018) and shows that drivers of such vehicles should be charged a much higher premium than any other groups.…”

Section: Discussionsupporting

confidence: 92%

Application of Machine Learning for Estimating Kenyan Motor Vehicle Insurance Premium

Pamba¹

2019

12th IADIS International Conference Information Systems 2019

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Motor vehicle damage insurance is the most common type of insurance in the world and one that generates the largest amount of loss for most insurance companies. In Kenya especially, the challenge faced by insurers is to balance the growth of the motor vehicle insurance business by increasing the customer base while also maintaining the profitability of this sector. It is crucial to identify the main causes of motor vehicle damage, its impact on revenue for insurers and factors that contribute to high motor claims to enable more accurate estimates of risk versus premium paid. In recent years the interest has increased in the use of information technology (IT) and statistical machine learning methods, supported by increasing computing capabilities, data availability and the trend towards automation. Statistical regression models have numerous applications in this regard. This paper explores applicability of new machine learning techniques such as tree-boosted models to optimize the proposed premium of prospective policy holders. It proposes two machine learning models for pricing motor vehicle damage insurance (decision trees and regression). The aim is to identify sources of risks in motor vehicles and the variables for motor vehicle premium determination. Data from insurance companies has been used, which is made up of the premium rates and compensations, and other variables such as age, driver's experience, etc. Results will be used to advise the insurance companies on how to charge premiums dynamically.

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Section: Discussionsupporting

confidence: 92%

Application of Machine Learning for Estimating Kenyan Motor Vehicle Insurance Premium

Pamba¹

2019

12th IADIS International Conference Information Systems 2019

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“…Collectively, these findings would suggest that low-value penalties are likely to offer a more cost-effective means of reducing risky driving behaviour than high-value rewards. These design principles are currently being used to optimise the design of financial incentives embedded within PAYD insurance, with their impact on the driving behaviour of novice drivers to be evaluated in on-road trials (Stevenson et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

The effect of ‘smart’ financial incentives on driving behaviour of novice drivers

Mortimer

Wijnands

Harris

et al. 2018

Accident Analysis & Prevention

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Recent studies have demonstrated that financial incentives can improve driving behaviour but high-value incentives are unlikely to be cost-effective and attempts to amplify the impact of low-value incentives have so far proven disappointing. The present study provides experimental evidence to inform the design of 'smart' and potentially more cost-effective incentives for safe driving in novice drivers. Study participants (n = 78) were randomised to one of four financial incentives: high-value penalty; low-value penalty; high-value reward; low-value reward; allowing us to compare high-value versus low-value incentives, penalties versus rewards, and to test specific hypotheses regarding motivational crowding out and gain/loss asymmetry. Results suggest that (i) penalties may be more effective than rewards of equal value, (ii) even low-value incentives can deliver net reductions in risky driving behaviours and, (iii) increasing the dollar-value of incentives may not increase their effectiveness. These design principles are currently being used to optimise the design of financial incentives embedded within PAYD insurance, with their impact on the driving behaviour of novice drivers to be evaluated in on-road trials.

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“…This feedback is delivered as a weekly report via the smartphone application. Using the simulation-based approach, [36] experimented with sending safety messages to drivers and proved that in-vehicle telematics can play a pivotal role for novice drivers in relation to their driving behaviors.…”

Section: The Dissection Of Relationships Among Main Stakeholdersmentioning

confidence: 99%

An Acceptance Approach for Novel Technologies in Car Insurance

et al. 2020

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Background: Unlike other financial services, technology-driven changes in the insurance industry have not been a vastly explored topic in scholarly literature. Incumbent insurance companies have hitherto been holding their positions using the complexity of the product, heavy regulation, and gigantic balance sheets as paramount factors for a relatively slow digitalization and technological transformation. However, new technologies such as car telematic devices have been creating a new insurance ecosystem. The aim of this study is to assess the telematics technology acceptance for insurance purposes. Methods: The study is based on the Unified Theory of Acceptance and Use of Technology (UTAUT). By interviewing 502 new car buyers, we tested the factors that affect the potential usage of telematic devices for insurance purposes. Results: The results indicate that facilitating conditions are the main predictor of telematics use. Moreover, privacy concerns related to the potential abuse of driving behavior data play an important role in technology acceptance. Conclusions: Although novel insurance technologies are mainly presented as user-driven, users (drivers and insurance buyers) are often neglected as an active party in the development of such technologies.

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The effects of feedback and incentive-based insurance on driving behaviours: study approach and protocols

Cited by 17 publications

References 29 publications

Application of Machine Learning for Estimating Kenyan Motor Vehicle Insurance Premium

Application of Machine Learning for Estimating Kenyan Motor Vehicle Insurance Premium

The effect of ‘smart’ financial incentives on driving behaviour of novice drivers

An Acceptance Approach for Novel Technologies in Car Insurance

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