Ce manuel comporte cinq parties à la structure similaire?: exposé du sujet, concepts explicatifs, techniques et modélisations, résumé. La première partie traite de la théorie des risques. Elle propose une définition des notions de risque et d'aléa puis une classification des différents types de risques et un exposé des théories et des pratiques de gestion et de transfert des risques. La deuxième partie est consacrée aux principes fondamentaux de l'assurance qui structurent ce secteur d'activité?: aversion au risque, assurabilité, inversion du cycle de production, mutualisation et segmentation. L'assurance est une activité financière et, à ce titre, fortement régulée. La troisième partie porte sur la régulation, dans ses fondements théoriques et sa mise en oeuvre pratique sur divers marchés, particulièrement sur le marché européen. La quatrième partie expose les principales techniques utilisé
This book offers an introduction to the technical foundations of discrimination and equity issues in insurance models, catering to undergraduates, postgraduates, and practitioners. It is a self-contained resource, accessible to those with a basic understanding of probability and statistics. Designed as both a reference guide and a means to develop fairer models, the book acknowledges the complexity and ambiguity surrounding the question of discrimination in insurance. In insurance, proposing differentiated premiums that accurately reflect policyholders' true risk-termed "actuarial fairness" or "legitimate discrimination"-is economically and ethically motivated. However, such segmentation can appear discriminatory from a legal perspective. By intertwining real-life examples with academic models, the book incorporates diverse perspectives from philosophy, social sciences, economics, mathematics, and computer science. Although discrimination has long been a subject of inquiry in economics and philosophy, it has gained renewed prominence in the context of "big data," with an abundance of proxy variables capturing sensitive attributes, and "artificial intelligence" or specifically "machine learning" techniques, which often involve less interpretable black box algorithms.The book distinguishes between models and data to enhance our comprehension of why a model may appear unfair. It reminds us that while a model may not be inherently good or bad, it is never neutral and often represents a formalization of a world seen through potentially biased data. Furthermore, the book equips actuaries with technical tools to quantify and mitigate potential discrimination, featuring dedicated chapters that delve into these methods.
