Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques

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dc.contributor.authorPaula, Daniel Abreu Vasconcellos de
dc.contributor.authorRINALDO ARTES
dc.contributor.authorAyres, Fabio
dc.contributor.authorANDREA MARIA ACCIOLY FONSECA MINARDI
dc.coverage.paisBrasilpt_BR
dc.creatorPaula, Daniel Abreu Vasconcellos de
dc.creatorAyres, Fabio
dc.date.accessioned2022-08-06T13:30:27Z
dc.date.available2022-08-06T13:30:27Z
dc.date.issued2019
dc.description.abstractPurpose – Although credit unions are nonprofit organizations, their objectives depend on the efficient management of their resources and credit risk aligned with the principles of the cooperative doctrine. This paper aims to propose the combined use of credit scoring and profit scoring to increase the effectiveness of the loan-granting process in credit unions. Design/methodology/approach – This sample is composed by the data of personal loans transactions of a Brazilian credit union. Findings – The analysis reveals that the use of statistical methods improves significantly the predictability of default when compared to the use of subjective techniques and the superiority of the random forests model in estimating credit scoring and profit scoring when compared to logit and ordinary least squares method (OLS) regression. The study also illustrates how both analyses can be used jointly for more effective decision-making. Originality/value – Replacing subjective analysis with objective credit analysis using deterministic models will benefit Brazilian credit unions. The credit decision will be based on the input variables and on clear criteria, turning the decision-making process impartial. The joint use of credit scoring and profit scoring allows granting credit for the clients with the highest potential to pay debt obligation and, at the same time, to certify that the transaction profitability meets the goals of the organization: to be sustainable and to provide loans and investment opportunities at attractive rates to members.pt_BR
dc.format.extentp. 321-336pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.doihttps://doi.org/10.1108/RAUSP-03-2018-0003pt_BR
dc.identifier.issn2531-0488pt_BR
dc.identifier.issue3pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/3888
dc.identifier.volume54pt_BR
dc.language.isoInglêspt_BR
dc.publisherEmerald Publishing Limitedpt_BR
dc.relation.isboundProdução vinculada ao Núcleo de Ciências de Dados e Decisão
dc.relation.ispartofRAUSP Management Journalpt_BR
dc.rights.licenseO INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR.pt_BR
dc.subjectCredit unionspt_BR
dc.subjectcredit scoringpt_BR
dc.subjectprofit scoringpt_BR
dc.subjectRandom forestpt_BR
dc.titleEstimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniquespt_BR
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://www.emerald.com/insight/content/doi/10.1108/RAUSP-03-2018-0003/full/html
local.subject.cnpqCiências Exatas e da Terrapt_BR
local.typeArtigo Científicopt_BR
relation.isAuthorOfPublication8b791c94-f3e5-4e04-af26-594195a8f576
relation.isAuthorOfPublication4f89a841-117c-473d-8798-96eb2d9ce1cf
relation.isAuthorOfPublication.latestForDiscovery4f89a841-117c-473d-8798-96eb2d9ce1cf
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