Detalhes bibliográficos
| Ano de defesa: |
2017 |
| Autor(a) principal: |
Silva, Priscila Cristina |
| Orientador(a): |
Sassi, Renato José |
| Banca de defesa: |
Sassi, Renato José,
Dias, Cleber Gustavo,
Schimit, Pedro Henrique Triguis |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Nove de Julho
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação de Mestrado e Doutorado em Engenharia de Produção
|
| Departamento: |
Engenharia
|
| País: |
Brasil
|
| Palavras-chave em Português: |
|
| Palavras-chave em Inglês: |
|
| Área do conhecimento CNPq: |
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| Link de acesso: |
http://bibliotecatede.uninove.br/handle/tede/1702
|
Resumo: |
Customers with credit agreement in arrears for more than 90 days are characterized as non-performing loans and cause concerns in credit companies because the lack of guarantee of discharge debtor's amount. To treat this type of customer are applied collection scoring models that have as main objective to predict those debtors who have propensity to honor their debts, that is, this model focuses on credit recovery. Models based on statistical prediction techniques can be applied to the recovery of these credits, such as logistic regression and discriminant analysis. Therefore, the aim of this paper was to apply logistic regression and discriminant analysis models in predicting the recovery of non-performing loans credit portfolios. The database used was provided by the company Serasa Experian and contains a sample of ten thousand customers with twenty independent variables and a variable binary response (dependent) indicating whether or not the defaulting customer paid their debt. The sample was divided into training, validation and test and the models cited in the objective were applied individually. Then, two new logistic regression models and discriminant analysis were implemented from the outputs of the individually implemented models. The both models applied individually as the new models had generally good performance form, highlighting the new model of discriminant analysis that got correct classification of percentage higher than the new logistic regression model. It was concluded, then, based on the results that the models are a good option for predicting the credit portfolio recovery. |
