Modeling corporate customers’ credit risk considering the ensemble approaches in multiclass classification: evidence from Iranian corporate credits

ABSTRACT The credit scoring system is one of the most significant credit risk control tools in;the banking industry. Usually, the existing credit scoring models classify customers;into "good credit" and "bad credit" groups. In this study, a novel model is proposed;to classify corporate client accounts into four groups - good credit, past due, overdue;and doubtful - according to the definitions of the Central Bank of the Islamic Republic;of Iran. This model enables lenders to develop specific policies for credit granting by;predicting the solvency and insolvency rates of their corporate clients. For validation,;the proposed model, trained by the hybrid approach of a self-organizing map and;radial basis function (RBF) neural network, is compared with a single-step, four-class;classification model and a model trained by support vector machines. The results show;that the proposed model trained by the hybrid approach of a self-organizing map and;RBF neural network outperforms the existing methods in terms of its final accuracy;with regard to the four classes at the test stage.