Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques
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Artigo Científico
Data
2019
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Purpose – 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.
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Vínculo institucional
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RAUSP Management Journal
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Membros da banca
Área do Conhecimento CNPQ
Ciências Exatas e da Terra