FREZAL Sylvestre

The use of statistical magnitudes to inform decision-making in risk situations, which first appeared in the 18th century and was then disqualified in the 19th century, was reintroduced in the middle of the 20th century and has since then gradually gained acceptance in the financial industry, percolating through the insurance industry to the point where it has become widespread via Solvency 2. However, numerous dysfunctions linked to the mobilization of these tools for the management and regulation of risks have been highlighted by the economic, actuarial and management science literature. Consequently, this extension of the scope of application of such tools to the regulation of insurance raises questions. This thesis (i) empirically extends to the insurance sector the literature produced in other sectors in order to identify the limits of the use of statistics for risk management and prudential regulation, (ii) proposes a unified theoretical framework for the dysfunctions linked to these uses, and (iii) sheds light on the reasons for the adoption of these tools within the insurance sector.

No summary available.

The use of statistical magnitudes to inform decision-making in risk situations, which first appeared in the 18th century and was then disqualified in the 19th century, was reintroduced in the middle of the 20th century and has since then gradually gained acceptance in the financial industry, percolating through the insurance industry to the point where it has become widespread via Solvency 2. However, numerous dysfunctions linked to the mobilization of these tools for the management and regulation of risks have been highlighted by the economic, actuarial and management science literature. Consequently, this extension of the scope of application of such tools to the regulation of insurance raises questions. This thesis (i) empirically extends to the insurance sector the literature produced in other sectors in order to identify the limits of the use of statistics for risk management and prudential regulation, (ii) proposes a unified theoretical framework for the dysfunctions linked to these uses, and (iii) sheds light on the reasons for the adoption of these tools within the insurance sector.

No summary available.

How can we understand that at the heart of the mathematical tools used daily by financial actors is the assimilation of two situations - situations of randomness and heterogeneity - that statistical tools very unequally allow to grasp? The article traces the way in which the amalgam between situations of randomness and situations of heterogeneity comes to play the role of an institution within the insurance sector, distinguishing, within this process of institutionalization, the moments of underground appropriation and the times of mobilization. The amalgam was first slowly appropriated by financial market and banking professionals, and it was from these activities, which were contiguous to it, that it entered the insurance sector. Two episodes of mobilization were then decisive, one concerning the valuation of companies involved in merger-acquisition operations, the other concerning the implementation of new prudential rules. It was then reintroduced as an institution in the practice of the actors, who sometimes adhered to it unconditionally, and sometimes maintained a reflexive distance from it.

No summary available.

No summary available.

How can we understand that at the heart of the mathematical tools used daily by financial actors to guide their decisions is the assimilation of two situations - situations of randomness and heterogeneity - that statistical tools very unequally allow to grasp? The article traces the way in which the amalgam between situations of randomness and situations of heterogeneity has come to play the role of a determining, albeit implicit, institution within contemporary financial reasoning. We explore the two social scenes in which this assimilation is likely to be engaged: that of its theoretical uses, in the social space where probability theory is written and developed and the uses made of it in financial mathematics and economics; that of its practical uses, in the organizations and markets where the models it contributes to found are used, focusing more particularly on the insurance sector.