Data mining application in banking sector with clustering and classification methods

Because of the phenomenal rise in information, future forecasting systems about strategy development were needed in each area. Therefore, data mining techniques are used extensively in banking area such as many areas. In this study, conducted in banking sector, it was aimed to reduce the rate of risk in decision making to a minimum via analysis of existing personal loan customers and estimate potential customers´ payment performances with k-means method is one of the clustering techniques and the decision trees method which is one of the models of classification in data mining. In the study, SPSS Clementine was used as a software of data mining and an application was done for evaluation of personal loan customers.