Abstract
The paper described a new model based on rough sets and support vector machines (SVM) to evaluate credit risk in commercial banks. In the model.a index system is established, then the rough sets was used to reduce the number of indexes and to make the calculation easy. The SVM was used to classify the credit risk precisely. A real case is given to test the model and the experimental results show that the model has high accuracy.The paper also compared it with the backpropagation neural network(BPNN) method .The data showed that the new model based on rough sets and SVM is more precise and more efficient than the BPNN method. Those advantages proved that the new model is a more effective one for evaluating credit risk in commercial banks.
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