Credit cards constitute one of the most common forms of consumer loans. The main purpose of this paper is to apply fuzzy data analysis to the credit scoring problem. A neuro-fuzzy classification technique is compared to the logis- tic regression approach and novel machine learning algorithms that are currently being investigated as credit scoring methods. The 10-fold cross-validation procedure is performed to analyze the generalization properties and the robustness of the developed models. Neuro-fuzzy classification sys- tems allow for prior knowledge to be imbedded in the analysis and utilize human expertise in the form of fuzzy if then rules to provide an insight into the reasoning mechanism behind the credit approval/rejection decision. This feature is particularly useful in financial applications such as credit granting, where credit analysts should be in a position to provide an explanation for their decisions.