The adoption of Big Data analytics (BDA) in insurance has proved controversial but there has been little analysis specifying how insurance practices are changing. Is insurance passively subject to the forces of disruptive innovation, moving away from the pooling of risk towards its personalisation or individualisation, and what might that mean in practice? This special theme situates disruptive innovations, particularly the experimental practices of behaviour-based personalisation, in the context of the practice and regulation of contemporary insurance. Our contributors argue that behaviour-based personalisation in insurance has different and broader implications than have yet been appreciated. BDAs are changing how insurance governs risk; how it knows, classifies, manages, prices and sells it, in ways that are more opaque and more extensive than the black boxes of in-car telematics.
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.
In this article we suggest Actor-Network Theory (ANT) as an alternative perspective on the object of social sciences and its practices. It is often stated that sociologists and social scientists have a ‘societal responsibility’, and that social sciences could provoke the societal consciousness by showing society a mirror image. Showing a state of affairs could urge politicians and other stakeholders in policy-making to take action. In this respect, the sociologist can only observe ‘what is’; although the positing of a state of affairs can start up a societal debate, the sociologists does not have a possibility to intervene. What the sociological imagination is capable of, is (re)presenting uncomfortable truths: description precedes intervention. Developing the ANT-perspective, we argue that this power of imagination can, however, be broadened when sociology undergoes a self-investigation. Sociological research is capable of altering reality: performing research is performative. This claim makes that the responsibility of the social scientist goes beyond just ‘showing’ societal problems by putting society in front of a mirror. Sociological description is in itself an intervention. We support these claims by giving three examples: the European Social Survey (ESS) performs the European Citizen, the data-infrastructure of Big Data challenges practices in the insurance market, and a controversy on a Belgian Voting Aid Application (‘Doe de Stemtest 2014’) makes clear that criteria of inclusion are never neutral.
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