2020
DOI: 10.1177/2053951720914650
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‘Happy failures’: Experimentation with behaviour-based personalisation in car insurance

Abstract: Insurance markets have always relied on large amounts of data to assess risks and price their products. New data-driven technologies, including wearable health trackers, smartphone sensors, predictive modelling and Big Data analytics, are challenging these established practices. In tracking insurance clients' behaviour, these innovations promise the reduction of insurance costs and more accurate pricing through the personalisation of premiums and products. Building on insights from the sociology of markets and… Show more

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Cited by 28 publications
(16 citation statements)
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“…aggregated search engine data including credit checks, license details, claims discount databases, price comparison website quote); social media data and connected device data. This means that insurers have a far, far greater range of 'behavioural' data sources than connected devices to draw upon and even connected device data is more nuanced in their application than the idea of behavioural pricing suggests (see Jeanningros and McFall, 2020;Meyers, 2018;Meyers and Hoyweghen, 2020). Credit information, for example, has been used to price motor and home insurance for several years despite its potential for proxy discrimination (Kiviat, 2019;Prince and Schwarcz, 2020).…”
Section: Data Behaviour and Innovation In Insurance Practice (On Selling Lemonade)mentioning
confidence: 99%
“…aggregated search engine data including credit checks, license details, claims discount databases, price comparison website quote); social media data and connected device data. This means that insurers have a far, far greater range of 'behavioural' data sources than connected devices to draw upon and even connected device data is more nuanced in their application than the idea of behavioural pricing suggests (see Jeanningros and McFall, 2020;Meyers, 2018;Meyers and Hoyweghen, 2020). Credit information, for example, has been used to price motor and home insurance for several years despite its potential for proxy discrimination (Kiviat, 2019;Prince and Schwarcz, 2020).…”
Section: Data Behaviour and Innovation In Insurance Practice (On Selling Lemonade)mentioning
confidence: 99%
“…We are not able to discern whether near‐miss telematics has any additional benefit over a distance‐based pricing scheme, and we are unable to say from our empirical analysis whether drivers adopting telematics schemes will in general change their behavior in the long term as a consequence of the impact on the price of their usage‐based insurance ratemaking. (see, Meyers & Van Hoyweghen, 2020, for an extensive economic experiment discussion).…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, Ellison et al (2015) conclude that personalized feedback alone is sufficient to induce significant changes in driving behavior, but that the largest reductions in risk are observed when drivers are also awarded a financial incentive to change. As part of a wide‐ranging research project on behavior‐based personalization in European insurance, Meyers and Van Hoyweghen (2020) report a case study carried out in 2016 aimed at tracking the driving behavior of 5000 participants through smartphone sensors in return for a 20% discount on their premium. However, they only recruited 243 participants in the study, and no clear evidence on the relation between driving style and loss ratios was found.…”
Section: Near‐miss Telematics and Usage‐based Insurancementioning
confidence: 99%
“…They regard this as an issue of asymmetric information and moral hazard; without ostensibly objective measures, people might exaggerate their activities and undeservingly gain benefits from a policy. A similar focus on 'objective' data came up, for instance, in the case of a Belgian pay-as-you-drive car insurance experiment in which 'real' evidence on the effectiveness of the digital tools needed to be provided to fulfil regulatory requirements (Meyers and Van Hoyweghen 2020). This approach emphasises the role of financial rewards and treats bonuses as a policy's main motivating element.…”
Section: Incentivisingmentioning
confidence: 99%