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- Title
APLICAÇÃO DE REGRESSÃO LOGÍSTICA E ALGORITMOS GENÉTICOS NA ANÁLISE DE RISCO DE CRÉDITO.
- Authors
Gouvêa, Maria Aparecida; Gonçalves, Eric Bacconi; Mantovani, Daielly Melina Nassif
- Abstract
The taking of decisions of credit concession is based basically on the evaluation of the insolvency risk of potential contractors of credit products. With the technological advance, statistical models have been developed to support the analysis of credit requests, which was many times carried through qualitatively some decades ago. The goal of this study is to present the use of logistic regression and genetic algorithms for sorting good and bad payers in bank financing and the identification of the best model in terms of goodness-of-fit. From a sample of 14,000 data, supplied by a great Brazilian financial institution, the two techniques were applied. Logistic regression presented the best goodness-of-fit. This work illustrated the procedures to be adopted by a company to identify the best model of credit concession, from which it is possible to direct the strategy of the institution in the evaluation process of bank loan requests.
- Subjects
BRAZIL; CREDIT risk; LOGISTIC regression analysis; GENETIC algorithms; TECHNOLOGICAL innovations; FINANCIAL institutions
- Publication
Revista Universo Contábil, 2012, Vol 8, Issue 2, p84
- ISSN
1809-3337
- Publication type
Article
- DOI
10.4270/ruc.2012214