Abstract—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.
Index Terms—Credit Risk, Ensemble learning, Hybrid Model, Artificial Intelligence techniques.
A. Ghodselahi is with Tarbiat Modares University, Tehran, Iran. He is IACSIT member. (E-mail: Ahmad. Ghodselahi@ Gmail.com).
A. Amirmadhi is with the Industrial Management Department, Islamic Azad University, Science & research branch, Tehran, Iran. (E-mail: Ashkan. Amirmadhi@ Yahoo.com).
Cite: Ahmad Ghodselahi and Ashkan Amirmadhi, "Application of Artificial Intelligence Techniques for Credit Risk Evaluation," International Journal of Modeling and Optimization vol. 1, no. 3, pp. 243-249, 2011.
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