Governança em bancos de dados de análise de crédito para instituições financeiras a partir do uso da inteligência artificial e das decisões automatizadas: como a adequação interna pode contribuir para a correta definição do risco representado no score de crédito - Souza, Maique Barbosa de (Universidade do Vale do Rio dos Sinos, 2022)
This research seeks to find guidelines for the construction of a governance model in databases that, using artificial intelligence for automated processing, is capable of correctly defining the credit risk for the financial institution. For this, the problem was proposed in the sense of how to build a governance model in credit analysis databases that, using artificial intelligence for automated decisions, allows the correct definition of risk to the financial institution and is adequate to the protection legislation of Dice? To find possible answers to the problem, two hypotheses were established, the first being the data analysis, with the recommendation of measures to be adopted in the construction of the system and the second related to the adequacy of data protection regulation and how this can be transform it into an opportunity to implement a competitive advantage for the financial institution. With regard to the general objective, the research sought to identify which actions are adequate to avoid errors in the definition of credit risk, as well as which provide for the capture of value that is not yet appropriate in the process of adaptation to the protective legislation for personal data. The methodology is theoretical and descriptive, with research in books, periodicals, articles and academic and market publications on the subject. As a result of the research, attached to the dissertation, a framework is presented with the proposition of adequate measures for the construction of credit analysis databases with automated decision, hoping that it will be useful to the market and that it allows the proper interaction between academia and the banking sector.
Keywords: Governança; Inteligência artificial; Dados; Risco de crédito; Governance; Artificial intelligence; Data; Credit risk; ACCNPQ::Ciências Sociais Aplicadas::Direito