Classification of Credit Applicants of Banking Systems Using Data Mining and Fuzzy Logic

This research study aims at using Data Mining and Fuzzy Logicapproaches to classify the credit scoring of banking system applicantsas to cover uncertainties and ambiguity connected with applicantclasses and also variables that affect their behavior.The methodology, according to a standard Data Mining process, is tocollect and refine the client data, then those variables which are inlinguistic forms are converted to fuzzy variables under the supervisionof banking experts and final data are modeled using Fuzzy DecisionTree, subsequently. The unfuzzy data are also modeled using the otheralgorithms.The results of the study suggest that as far as client distinctionaccuracy is concerned Fuzzy Decision Tree produces better resultscompared to Traditional Trees, Neural Networks, and statisticalprocedures such as Logistic Regression and Bayesian Network.However, it is not as accurate as Support Vector Machine and GeneticTree. On the other hand, Fuzzy Decision Tree technique has gainedbetter prediction than prediction performance of bank credit scoringexperts.