Credit risk is the most challenging risk to which financial institution are exposed. Credit scoring is the main analytical technique for credit risk assessment. In this paper a hybrid model for credit scoring is designed which applies ensemble learning for credit granting decisions. The hybrid model combines clustering and classification techniques. Ten Support Vector Machine (SVM) classifiers are utilized as the members of ensemble model. Since even a small improvement in credit scoring accuracy causes significant loss reduction, then the application of ensemble in hybrid model leads to better performance of classification. A real dataset is used to test the model performance. The test results shows that proposed hybrid SVM ensemble has better classification accuracy when compared with other methods.

Credit risk is the most challenging risk to which financial institution are exposed. Credit scoring is the main analytical technique for credit risk evaluation. Application of artificial intelligence has lead to better performance of credit scoring models. In this paper a hybrid model for credit scoring is designed which applies ensemble learning for credit granting decisions. Ten classifier agents are utilized as the members of ensemble model. Support vector machine, Neural Networks and Decision Tree as base classifiers were compared based on their accuracy in classification. Since even a small improvement in credit scoring accuracy causes significant loss reduction, then the utilization of best classification model is of a great importance. A real dataset was used to test the model and classifiers. The test results showed that proposed hybrid ensemble model has better classification accuracy and performance when compared to other credit scoring methods. In addition, among three classifiers, the support Vector Machine had the best performance and accuracy.