Comparative performance of credit scoring models using aggregated predictors

The aim of this paper is to evaluate the results in term of misclassification rate of two classification models, Logit and Classification Trees (Cart), in a credit scoring context. Due to the dependence of results on input variables we will take into account this aspect to evaluate the prediction performance. To improve the prediction capability of this two models, we have also applied two statistical techniques, bagging and boosting, to evaluate whether using these aggregated predictors can be reached a better performance in term of classification results. Our results indicate a better classification capability of Cart and the error rate of both models can be further reduced using aggregated predictors. Furthermore Cart avoids variables selection problem.