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Scope: Commercial banks, as the key of the nation's economy and the center of financial credit, play a multiple irreplaceable role in the financial system. Credit risks threaten the economic system as a whole. Therefore, predicting bank financial credit risks is crucial to prevent and lessen the incoming negative effects on the economic system. Objective: This study aims to apply a credit risk assessment model based on support vector machines (SVMs) in a Chinese case, after analyzing the credit risk rules and building a credit risk system. After the modeling, it presents a comprehensive computational comparison of the classification performances of the techniques tested, including back-propagation neural network (BPN) and SVMs. Method: In this empirical study, we utilize statistical product and service solutions (SPSS) for the factor analysis on the financial data from the 157 companies and Matlab and Libsvm toolbox for the experimental analysis. Conclusion: We compare the assessment results of SVMs and BPN and get the indication that SVMs are very suitable for the credit risk assessment of commercial banks. Empirical results show that SVMs are effective and more advantageous than BPN. SVMs, with the features of simple classification hyperplane, good generalization ability, accurate goodness of fit, and strong robustness, have a better developing prospect although there are still some problems with them, such as the space mapping of the kernels, the optimizing scale, and so on. They are worthy of our further exploration and research.