Abstract: Aiming at the problem of outliers and noise in credit evaluation data, we proposed a support vector machine (SVM) credit risk evaluation model based on eliminating outliers. This model used Fuzzy c-means clustering algorithm to eliminate the outliers. The classification performance of SVM was impro
ved by optimizing the SVM classification parameters by using the particle swarm optimization algorithm. The results of applying the proposed method to the credit risk evaluation show that the classification accuracy is higher than other models. 

Key words: credit risk, outlier, Fuzzy c-means clustering algorithm, support vector machine (SVM)