Natural Customer Ranking of Banks in Terms of Credit Risk by Using Data Mining : A Case Study : Branches of Mellat Bank of Iran

ith the development of trade and business throughout the world, needs to become more widespread financial dealings that led to the development of the business activities of banks and new banks were established. Banking and financial systems, credit risk management is a major problem. So check applicants' credit facility to repay the process is important and many methods have been proposed for this work is the study of data mining techniques to identify genuine bank customer credit risk will be used. Numeric and non-numeric data is the selected data set consisting of 1000. This data set contains 20 features. 7 of the 20 features and 13 features a numerical and non-numerical attributes are nominal. Actually this is 20 characters are input. There's also a feature called Class, category and class of a row of characters in the show. (Ie the output of the problem) that are included in this category are 2 classes, good and bad. In the first 300 customer data poorly (Y = 1) and 700, proper and good customer (Y = 0) exist.