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

Imagem de Miniatura
Paula, Daniel Abreu Vasconcellos de
Ayres, Fabio
Tipo de documento
Artigo Científico
Título da Revista
ISSN da Revista
Título do Volume
Projetos de Pesquisa
Unidades Organizacionais
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.

Titulo de periódico
RAUSP Management Journal
Título de Livro
Membros da banca
Área do Conhecimento CNPQ
Ciências Exatas e da Terra