Segmentação do perfil de clientes inadimplentes utilizando ferramentas computacionais

• Autor(a) principal: Rocha, Evelyn
• Data de Publicação: 2022
• Tipo de documento: Trabalho de conclusão de curso
• Idioma: por
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/217472
• topic: Default; Logistic regression; Computational tools; Inadimplência (Finanças); Regressão logística; Ferramentas computacionais; Estatística; Análise de regressão logística; Big data; Programas de computador

Default is a term that is very present in the lives of Brazilians, since in 2021 Brazil had its highest average debt level in 11 years. In addition, credit collection and recovery areas sought to increasingly improve their methods of approaching the customer, always aiming at the best strategy and the pursuit of profit. From this, it is possible to see how many studies with a credit recovery approach are important to improve processes and provide preventive actions, being these objectives of this work. Throughout the text it is possible to understand the credit path or the credit collection and recovery process, understand how some computational tools work, succinctly absorb the resource theory in logistics in addition to seeing it in practice from a mixed approach. among other Python and R software. In those evaluated under construction, the pre-feasible model to segment the profile of customers into non-defaulting customers was shown to be viable, whether a digital profile or not.