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

Introduction Banks as the main part of the financial system has been one of the main risks that they face different credit risk. A significant volume of facilities or outstanding fuel banks, indicates a lack of appropriate models for credit risk measurement and risk management systems in the banking network [1, 3]. One of the most important tools banks need to manage and control credit risk it, is "customer credit rating system". Using the data mining analysis of information related to bank customers using credit ratings can be creditworthy loan applicants and classify them according to customer accounts and bad, without judgment on the basis of smart payment systems. So banks to determine the needs of its customers, the granting of credit facilities to identify the characteristics of them. This validation by a decrease in bank risk including credit risk is. Accreditation refers to the practice where customers natural and legal validity of financial institutions and bank credit is measured according to the information received from them. And the possibility of a better understanding of the financial situation and people to repay loans received and provide more services. According to this method, the credit risk is measured and customers based on their credit risk classification and grading are [7, 11]. Credit ratings, risk management tool that uses data and statistical techniques to rank the customer pays a little Applicants facilities. Credit ratings of the different attributes of the default of its loan applicants separately examined and calculate the probability of default of the loan applicant's credit ratings at risk of deals [16, 17]. Credit ratings of credit models into two groups: good and bad credit applicants divide. Good credit group, a group that will repay its debts on time and bad credit debt group that has a high probability they will default. Due to the dynamic development of the credit industry, today the industry has an important role in the economy. Although credit demand, increased competition and the development of new channels in the new economic environment, has In 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.