SESSION 13. Behaviour-based personalisation in contemporary insurance markets

13. Behaviour-based personalisation in contemporary insurance markets.

Gert Meyers (KU Leuven) gert.meyers@kuleuven.be
Ine Van Hoyweghen (KU Leuven)
Liz McFall (Open University)
Hugo Jeanningros (Université Paris-Sorbonne).


Big Data is promising a revolution in different societal spheres such as security, health and (online) shopping (Mayer-Schönberger & Cukier, 2013). Massive amounts of personal data (genetic information, shared information from wearable devices, internet behavior information) will become manageable in real-time now or in the near future. In the field of insurance, the best known example of Big Data is usage-based car insurance (Car UBI). Big Data – in the form of predictive modelling, Machine Learning (ML), Artificial Intelligence (AI), Internet of Things (IoT), and other technologies that enable the treatment of ‘personalised data’ – is considered to be a ‘disruptive technology’ (FINEOS, 2014; McKinsey Global Institute, 2013), altering the stabilised insurance practices of risk selection through the introduction of predictive data and modelling and the personalisation of risk.

Hypes and fears of the ‘disruptive’ potentials of predictive modelling in insurance abound. Big Data comes with the promise of reducing insurance costs, more accurate pricing and personalising risk, to support healthy lifestyles, make clients accountable, and/or secure their responsible behaviour celebrating predictive modelling solutions as the ‘new way to be smart’ (Ayres, 2007) or as a desirable shift because ‘the ongoing trends towards real-time risk assessment, product and process simplification and automation could accelerate moves towards more radical business models in insurance’ (Swiss Re 2017, 25). Others fear that predictive data and modelling in insurance would increase inequality and discrimination (O’Neil, 2016), resulting in ‘social sorting’ (Minty, 2014), ‘the end of solidarity’ that characterises European insurance markets (Gayant, 2015) as well as the end of insurance-‘as-we-know-it’ (Llull, 2016).

Never mind their respective intuitive merits, such claims on hypes and fears popularise the idea of big data as a paradigm shift. As such, they often neglect the way predictive data and modelling concretely affect, transform, disrupt or reinforce existing practices. In this session, we want to pay attention to contemporary insurance practices, and more specifically to experimental practices of behaviour-based personalisation. Our hypothesis is that behaviour-based personalisation, as a process driving the domain of insurance, does not simply increase the amount of available data and optimise the processes it is applied to, but also changes our ways of knowing, our ways of social ordering and the way we make decisions (Ewald 1991, 2012, Baker 2002, Meyers & Van Hoyweghen 2017). The practices of traditional insurance are being challenged by new forms and uses of data.

This session will accept contributions presenting research on the challenges surrounding behaviour-based personalisation in insurance, triggered by, but not restricted to, the following general question: how does behaviour-based personalisation affect existing insurance practices and institutions?

More precisely the following specified research questions will be discussed:

  • What is behaviour-based personalisation insurance?
  • Which are the technical, legal and social conditions and considerations for behaviour-based personalisation in insurance practices? How is ‘risk’ enacted as ‘personalised’ in insurance experiments?
  • Which are the technical, legal and social consequences of behaviour-based personalisation in insurance practices?
  • How does the emergence of behaviour-based personalisation in insurance reconfigure the role and responsibilities of the insurer and the insured, as well as the idea of ‘risk’? Can this be considered as a shift away from insurance-‘as-we-know-it’?
  • How does the emergence of behaviour-based personalisation in insurance reconfigure the role of insurance as an institution generating solidarity?

Main objectives

The session has four objectives. It will:

  • Empirically document current involved actors and discourses on behaviour-based personalisation in European insurance markets, as well as investigate its national and supranational regulatory contexts;
  • Empirically study behaviour-based personalisation – processes of data collection, curation, analysis and prediction, including their usage in insurance risk assessment processes – in insurance practices, in terms of its actuarial, legal and social aspects and consequences;
  • Reflect more broadly on the future role of insurance in an era of Big Data and its implications for discrimination, solidarity, and fairness.