Data mining application in the decision-making process: the case of credit risk assessment
2007
Salame, E. J.
The granting of loans by a financial institution (bank or any loan business) is one of the important decision problems that require delicate care. It can be performed using a variety of different processing algorithms and tools. Neural networks are considered to be one of the most promising approaches. Credit rating analysis has attracted a lot of research interests in the literature. Recent studies have shown that Artificial Intelligence (AI) methods achieve better performance than traditional statistical methods. This thesis introduces the relatively new machine learning techniques, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Probabilistic Neural Networks (PNN), and classical statistical methods such as Logistic Regression (Log R.) and Classification And Regression Trees (CART) to the problem in an attempt to provide a classification tool and to discriminate good creditors from bad ones. In this study SVM, ANN, PNN, CART and Log R. were used and classification accuracies of around 65% for both ANN and SVM methods for the training and testing were obtained. However, only a slight difference was observed among CART, PNN and Log R. Another direction of this research was to compare the different methods. Based on the results obtained, it was concluded that the ANN and SVM need no longer be compared with the traditional classification techniques, and that they represent a tool of reference in themselves.
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