Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques
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| Natura: | Artículo científico |
| Lingua: | en |
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Universidade de São Paulo
2019
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| _version_ | 1876442382360117248 |
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| author | Daniel Abreu Vasconcellos de Paula |
| author_facet | Daniel Abreu Vasconcellos de Paula |
| contents | Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques Daniel Abreu Vasconcellos de Paula Rinaldo Artes Fabio Ayres Andrea Maria Accioly Fonseca Minardi Administración y Contabilidad Credit unions 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. 2019 artículo científico 2531-0488 https://www.redalyc.org/articulo.oa?id=553860104006 https://www.redalyc.org/journal/5538/553860104006/ https://www.redalyc.org/journal/5538/553860104006/html/ https://www.redalyc.org/journal/5538/553860104006/553860104006.epub https://www.redalyc.org/journal/5538/553860104006/movil en http://www.redalyc.org/revista.oa?id=5538 RAUSP Management Journal application/pdf Universidade de São Paulo RAUSP Management Journal (Brasil) Num.3 Vol.54 |
| format | Artículo científico |
| id | redalyc_553860104006 |
| institution | Redalyc |
| language | en |
| publishDate | 2019 |
| publisher | Universidade de São Paulo |
| spellingShingle | Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques Daniel Abreu Vasconcellos de Paula Administración y Contabilidad Credit unions Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques Daniel Abreu Vasconcellos de Paula Rinaldo Artes Fabio Ayres Andrea Maria Accioly Fonseca Minardi Administración y Contabilidad Credit unions 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. 2019 artículo científico 2531-0488 https://www.redalyc.org/articulo.oa?id=553860104006 https://www.redalyc.org/journal/5538/553860104006/ https://www.redalyc.org/journal/5538/553860104006/html/ https://www.redalyc.org/journal/5538/553860104006/553860104006.epub https://www.redalyc.org/journal/5538/553860104006/movil en http://www.redalyc.org/revista.oa?id=5538 RAUSP Management Journal application/pdf Universidade de São Paulo RAUSP Management Journal (Brasil) Num.3 Vol.54 |
| title | Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques |
| topic | Administración y Contabilidad Credit unions |
| url | https://www.redalyc.org/articulo.oa?id=553860104006 https://www.redalyc.org/journal/5538/553860104006/ https://www.redalyc.org/journal/5538/553860104006/html/ https://www.redalyc.org/journal/5538/553860104006/553860104006.epub https://www.redalyc.org/journal/5538/553860104006/movil |