THE DEVELOPMENT OF CREDIT SCORING MODELS WITH LOGISTIC REGRESSION AND DISCRIMINANT ANALYSIS FOR THE CREDIT RISK MANAGEMENT OF A MICROCREDIT INSTITUTION

Abstract

The Credit Scoring models are quantitative ones usually used by financial institutions in measure and credit risk forecast, owning consolidated application during the credit concession process of these institutions. This work objectivies to evaluate the possibility of Credit Scoring models application in a microcredit institution denominated Fundo Rotativo de Ação da Cidadania – Cred Cidadania. For this purpose, some data relative to a clients' sample Cred Cidadania were collected and used to develop two Credit Scoring model types: one relating to credit approval and another one named behavioural scoring. The statistical  technique used in the models construction was logistic regression. The study results demonstrated that Credit Scoring models obtain satisfactory performance when used in the Cred Cidadania microcredit institution credit risk analysis, as supporting instruments to rely this process. The results also indicate that Credit Scoring models application supplies subsidies to the institution, assisting it in the prevention and reduction of its insolvency as in the decrease of its operational costs, two problems that affect its financial sustainability.