Diferenciação admissível e discriminação inadmissível no contrato de seguro privado: exame da precificação com base no gênero e em variáveis que causem impacto desproporcional nos indivíduos negros

• Autor(a) principal: Junqueira, Thiago Villela Bastos
• Data de Publicação: 2020
• Outros Autores: [e-mail]
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UERJ
• Texto Completo: http://www.bdtd.uerj.br/handle/1/17629
• topic: Insurance contract; Risk classification; Pricing; Differentiation and discrimination; Genre; Black race; Big data; Algorithm; Reasonable adjustment; Contrato de seguro; Classificação dos riscos; Precificação; Diferenciação e discriminação; Gênero; Raça negra; Big data; Algoritmo; Adaptação razoável; CIENCIAS SOCIAIS APLICADAS::DIREITO::DIREITO PRIVADO::DIREITO CIVIL

The thesis aims at analyzing, from a non-discrimination point of view, the pricing of private individual insurance contracts considering the profile of the (candidate) insured person. Thus, it starts showing that the risk classification by the insurance company has economic and social backgrounds and is supported by legal and regulatory provisions on the matter in Brazil. Then, the study moves to the questioning of the criteria used for such classification, using as support the prohibition of discrimination outlined in the Brazilian Federal Constitution (CF) and General Data Protection Regulation (LGPD). It should be highlighted that, in view of the ongoing factual- and law-related changes, the approach is divided into two distinct periods. In the first period, referred to as the “era of the insurance actuarial science”, it is analyzed the occurrence of possible direct genre discriminations in vehicle insurances. The subsequent chapter is dedicated to the investigation of promises and threats related to the expansion of Big Data and artificial intelligence in insurance underwriting; the ambiguous relationship, in the matter referred to herein, between privacy protection and fight against discrimination and the way the LGPD addresses crucial points involving them. Finally, possible strategies to prevent algorithmic discrimination in the (future) “era of insurance data science” are outlined, particularly when it comes to indirect discrimination against black people