The importance to financial institutions of accurately evaluating the credit risk posed by their loan granting decisions cannot be underestimated; it is underscored by recent credit assessment failures that contributed greatly to the so-called "great recession" of the late 2000s. The paper compares the classification accuracy rates of several traditional and computational intelligence methods. We construct models and assess their classification accuracy rates on five very versatile real world data sets obtained from different loan granting decision areas. The results obtained from computer experiments provide a fruitful ground for interpretation.
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