Using Data Mining to Predict Bank Customers Churn

Subject Areas : Industrial Management

parvin  najmi ¹ (Tehran North branch, Islamic Azad University, Tehran, Iran)
abbas  rad ² (Assistant Professor, Faculty of Management, Shahid Beheshti University)
maryam  shoar ³ (Dr. Maryam Shahram Assistant Professor, Faculty of Management, Islamic Azad University, Tehran North)

Received: 2018-04-30 Accepted : 2018-11-02 Published : 2018-08-23

Keywords: Neutral analysis, Neural network, Support vector machine, Decision tree,

Abstract :

The intensity of finding competition in the industrial and economic space and the market move towards a complete competition market has made the inclination of firms to attract more customers and, instead, have increased the tendency to operate in various service and manufacturing areas. This policy, which is known for increasing the share of wallet, makes it more important to maintain customer relationships and analyze their relationships, and it is necessary to conduct customer behavioral analysis, customer relationship analysis, and customer behavior forecasting. The present research seeks to identify customers who are turning away and anticipates the decline of customers in order to prevent customers from falling. In this regard, the variables associated with the reversal analysis are first identified and then the bank customers are clustered using a neural network and classified into three categories of loyal, regular, and negative clients. With the receipt of the above labels, a backup vector machine has been used to classify and reverse prediction. Based on the results, the proposed method has the ability to predict rotational deviation of up to 80% and, moreover, has a better performance than the classical decision tree.

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