Abstract
Nowadays, supervised learning algorithms have been gaining relevance in the context of Credit Scoring. However, the databases used for Credit Scoring have few examples of defaulters, which can lead the learning models to make classification errors, classifying a defaulter as a non-defaulter and consequently causing losses to the lender. Therefore, this study aims to investigate two approa…