A Hybrid Neural Network Ensemble Model for Credit Risk Assessment

Banking is a specific industry that deals with capital and risk for making profit. Credit risk as the most important risk، is an active research domain in financial risk management studies. In this paper a hybrid model for credit risk assessment which applies ensemble learning for credit granting decisions is designed. Combining clustering and classification techniques resulted in system improvement. The German bank real dataset was used for neural network training. The proposed model implemented as credit risk evaluation multi agent system and the results showed the proposed model has higher accuracy، better performance and lesser cost in applicant classification when compared with other credit risk evaluation methods.