‘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 Science and Technology Studies (STS), this article investigates the role of economic experimentation in the making of data-driven personalisation markets in insurance. We document a case study of a car insurance experiment, launched by a Belgian direct insurance company in 2016 to set up an experiment of tracking driving style behavioural data of over 5000 participants over a one-year period. Based on interviews and document analysis, we outline how this in vivo experiment was set-up, which interventions and manipulations were imposed to make the experiment successful, and how the study was evaluated by the actors. Using JL Austin's distinction between happy and unhappy statements, we argue how the experiment, despite its failure not to provide the desired evidence (on the link between driving style behaviour and accident losses), could be considered a 'happy' event. We conclude by highlighting the role of economic experiments 'in the wild' for the making of future markets of data-driven personalisation.

Editorial: The personalisation of insurance: Data, behaviour and innovation

“…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).…”

Near‐miss telematics in motor insurance

“…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).…”

“…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.…”

Tracking lives, forging markets

“…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.…”