Presentation of a Two Stages Model Based on Data Mining for Evaluation of Common Customers of Bank and Insurance Companies

Subject Areas : Financial Knowledge of Securities Analysis

Hamidreza Amir hasankhani ¹ , abbass toloie ² , Alireza poorebrahimi ³ , reza radfar ⁴

1 - PhD Candidate of IT Management Department, Emirates Branch, Islamic Azad University, Dubai, UAE
2 - Professor of Industrial Management Department, School of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran. (Corresponding Author):
3 - Assistant Professor of Management Department, School of Management, Karaj Branch, Islamic Azad University, Karaj, Iran
4 - Professor of Industrial Management Department, School of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran. (Corresponding Author):

Received: 2021-09-25 Accepted : 2021-09-25 Published : 2021-08-23

Keywords: Customer Relationship Manageme, data mining, Bank, insurance,

Abstract :

Exploration of knowledge from database and data mining is one of the most important tools for customer relationship management Which can help the organization to find useful information or their interesting knowledge. Today, banks and insurance companies have numerous and extensive databases that contain information about exchanges and other details related to their customers. Valuable business information can be retrieved from these data warehouses. However, support for such analyzes and decision-making will not be possible using traditional reporting languages. Therefore, considering the importance of the common customer’s information of the bank and insurance, they should be analyzed as carefully as possible. In this research, by collecting and analyzing the information of joint customers of the bank and insurance, a methodology based on the data mining is presented to evaluate customers according to their functional indicators in the field of banking and insurance. We will also predict the behavior of new customers by analyzing historical customer behavior using a two-step approach based on unsupervised learning and supervised learning.

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