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After the greatest financial debacle since the great depression, the need for accurate and systematic assessment of loan granting decisions has never been more important than now. The paper compares the classification accuracy rates of six models: logistic regression (LR), neural network (NN), radial basis function neural network (RBFNN), support vector machine (SVM), k-Nearest Neighbor (kNN), and decision tree (DT) for loan granting decisions. We build models and test their classification accuracy rates on five very versatile data sets drawn from different loan granting decision contexts. The results from computer simulation constitute a fertile ground for interpretation. [ABSTRACT FROM AUTHOR]