The aim of this article is to assess the impact of Big Data technologies for insurance ratemaking, with a special focus on motor products.The first part shows how statistics and insurance mechanisms adopted the same aggregate viewpoint. It made visible regularities that were invisible at the individual level, further supporting the classificatory approach of insurance and the assumption that all members of a class are identical risks. The second part focuses on the reversal of perspective currently occurring in data analysis with predictive analytics, and how this conceptually contradicts the collective basis of insurance. The tremendous volume of data and the personalization promise through accurate individual prediction indeed deeply shakes the homogeneity hypothesis behind pooling. The third part attempts to assess the extent of this shift in motor insurance. Onboard devices that collect continuous driving behavioural data could import this new paradigm into these products. An examination of the current state of research on models with telematics data shows however that the epistemological leap, for now, has not happened.

No abstract

Abstract This paper aims to show how insurance mechanisms that historically propelled a conception of fairness based on solidarity and a collective approach shifted along the 20th century towards an idealistic adjustment to individual risk. Insurance originally assumed that, while individual hazards remained unknown, risk could be measured and managed on the aggregate. An examination of the proceedings of the American Casualty Actuarial Society (CAS) during the 20th century demonstrates the slow crystallization of another conception of fairness, that aims at a scientific adjustment of insurance premiums to actual “individual risks.” I argue that this conception of fairness deconstructs the one based on solidarity. Big data technologies have further radicalized this shift. By aiming at predictive individual risk scores rather than average costs estimated on the aggregate, the algorithms contribute to replacing fairness as solidarity by the correctness of a computation.