Understanding Insurance in the Era of Big Data and Personalization of Risk

The Ivory Tower can't keep ignoring Tech" (O'Neill, 2017). This New York Times op-ed calling for interdisciplinary research on the accountability of big data and machine learning could not be more timely. As in many other domains of social life, hypes and fears on the ‘disruptive’ potential of big data in insurance abound. The use of fine-grained data and machine learning in insurance can result in the ‘personalization of risk’, posing important societal and regulatory challenges on issues of discrimination, privacy, accountability and fairness. To move beyond these hypes and fears, robust empirical research is urgently needed. This project aims to fill this gap by developing a strong interdisciplinary research program to investigate big data-enabled ‘personalization of risk’ in insurance and study the societal dimensions of big data technologies. To go beyond the idea of a simple, unidirectional ‘adoption’ of big data in insurance, we draw upon conceptual resources stemming from the interdisciplinary field of science and technology studies (STS). The project will investigate real-time experimental practices of big data-enabled personalization of risk in car and health insurance, drawing upon multiple research methods (literature review, fieldwork, interviews, interdisciplinary meetings and workshop), while instigating collaborative work across the disciplines of actuarial science, law, and social sciences. The interdisciplinary approach is original in bringing out the context-specificity of big data in the practices of insurance. This has so far received little attention in the literature and the collaboration between actuarial scientists, humanities and law scholars adds a unique opportunity to detect and frame how big data transforms the way we know and are acted upon in insurance. The project will be used to strengthen the collaboration within the interdisciplinary KU Leuven LRisk Center and concrete plans will be made for follow-up research.