Integrating Data Mining and Knowledge Management to Improve Customer Relationship Management in Banking Industry (Case Study of Caspian Credit Institution)

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Banks and financial - credit institutions by customer relationship management database analysis can manage to identify customers and allocate resources to profitable customers in a better manner. This study aims to find an index for customers, using customer characteristics to identify significant profitable customers of banking system. In this way we will be able to provide them with more adequate facilities. To do so, mix method of fuzzy clustering and imperialist competitive algorithm has been applied to accomplish customer data clustering; then Fuzzy C-Means and criteria of sum of intra – cluster distances were used to evaluate the method. Results display minimum and maximum as 39.270153 and 53.100917 respectively which at least are 6000 units less than the compared method. This indicates advantage of the suggested method compare to the other.

Published In : IJCSN Journal Volume 7, Issue 3

Date of Publication : June 2018

Pages : 208-214

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Tables : 02

Mohammad Ordouei : Computer Engineering Dep., Islamic Azad University, Central Tehran Branch (IAUCTB) Tehran, Iran.

Dr. Touraj BaniRostam : Computer Engineering Dep., Islamic Azad University, Central Tehran Branch (IAUCTB) Tehran, Iran.

Data Mining, CRM

In this study a clustering based mix method has been used to improve performance of customer relationship management in banking industry. After primary investigation on subject it was clarified that the combined algorithm of the study has not been used to improve customer relationship management and this is innovation of this study. To assimilate mix methods used in this research, MATLAB software has been applied. To evaluate the mix method, it was compared to sum of intra – cluster distances. Considering results revealed that the mix method had yielded more acceptable data mining results and it had performed better than fuzzy c- means. Furthermore it was concluded that people aged 30- 50 with B.A. degree or upper, average income higher than 3 millions, high account stock, less individual under care, had more transactions. Also single ones, owning residency with account stock more than 10 millions.

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