This document discusses using support vector machines (SVMs) to assess credit risk with imbalanced data sets. SVMs have limited performance with imbalanced credit data where unpaid loans are less frequent than paid loans. The author develops an SVM model using two data resampling techniques - random oversampling and SMOTE - to address class imbalance. Performance is evaluated using various criteria like accuracy, sensitivity, specificity, and AUC. The results suggest resampling data can improve SVM performance for accurate credit risk prediction with imbalanced data.