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dc.creatorSousa, Marcos de Moraes-
dc.creatorFigueiredo, Reginaldo Santana-
dc.date.accessioned2019-06-13T14:31:26Z-
dc.date.available2019-06-13T14:31:26Z-
dc.date.issued2014-08-
dc.identifier.citationSOUSA, Marcos de Moraes; FIGUEIREDO, Reginaldo Santana. Credit analysis using data mining: application in the case of a credit union. Journal of Information Systems and Technology Management, São Paulo, v. 11, n. 2, p. 379, May/Aug. 2014.pt_BR
dc.identifier.issne- 1807-1775-
dc.identifier.urihttp://repositorio.bc.ufg.br/handle/ri/17649-
dc.description.abstractThe search for efficiency in the cooperative credit sector has led cooperatives to adopt new technology and managerial knowhow. Among the tools that facilitate efficiency, data mining has stood out in recent years as a sophisticated methodology to search for knowledge that is “hidden” in organizations' databases. The process of granting credit is one of the central functions of a credit union; therefore, the use of instruments that support that process is desirable and may become a key factor in credit management. The steps undertaken by the present case study to perform the knowledge discovery process were data selection, data pre-processing and cleanup, data transformation, data mining, and the interpretation and evaluation of results. The results were evaluated through cross-validation of ten sets, repeated in ten simulations. The goal of this study is to develop models to analyze the capacity of a credit union's members to settle their commitments, using a decision tree—C4.5 algorithm and an artificial neural network—multilayer perceptron algorithm. It is concluded that for the problem at hand, the models have statistically similar results and may aid in a cooperative's decision-making process.pt_BR
dc.language.isoengpt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectCredit unionismpt_BR
dc.subjectData miningpt_BR
dc.subjectDecision treept_BR
dc.subjectArtificial neural networkpt_BR
dc.titleCredit analysis using data mining: application in the case of a credit unionpt_BR
dc.typeArtigopt_BR
dc.publisher.countryBrasilpt_BR
dc.identifier.doi10.4301/S1807-17752014000200009-
dc.publisher.departmentEscola de Agronomia e de Engenharia de Alimentos - EA (RG)pt_BR
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