Aplicação estruturada de dados de redes sociais na modelagem de instrumentos de apoio às decisões de concessão de crédito

• Ano de defesa: 2015
• Autor(a) principal: Fattibene, Marcos
• Orientador(a): Meirelles, Jorge Luís Faria
• Banca de defesa: Périco, Ana Elisa , Carvalho, Flávio Leonel de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Engenharia de Produção - PPGEP-So
• País: BR
• Palavras-chave em Português: Análise de redes sociais; Análise de crédito; Modelagem estatística; Regressão linear múltipla; Redes sociais on-line; Modelos lineares (Estatística)
• Palavras-chave em Inglês: Social networks analysis; Credit analysis; Statistical modeling; Multiple linear regression
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA DE PRODUCAO::ENGENHARIA ECONOMICA
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/3835

The credit analysis for individuals has traditionally relied on three pillars: documentary proof of income and residence; refers to negative credit bureaus as SERASA and SCPC and the use of forecasting models based on the hypothesis that similar profiles in the future will reproduce the same credit behavior of the past, such as the "credit scores" (HAND; HENLEY, 2007) . This approach has been adequate, while being susceptible to moments of economic crisis or to fast profile changing of the target market, as occurred in the U.S. subprime in 2008. This study aims to point out ways to use Social Networks informational content, where individuals express and record their opinions, preferences, and especially get evident their network of relationships, in the credit analysis context. It was made evident the feasibility to investigate the assumption that an individual's proximity to other appropriate profile payers, or vice versa, influences the repayment rate. To illustrate such a conclusion, a real social network, enriched with credit data obtained by statistical simulation, was used. Three models of data weighting and three other based on multiple linear regression models were developed. In general the results were not statistically significant, by need to use a non-brazilian social network, as well synthetic data bureau score, since real information was not available in this country. It was shown a way to investigate the hypothesis that the informational content of a social network may generate greater efficiency into credit analysis when added to decision-making, operational and control systems of this segment.