Knowledge Discovery On Investment Fund Transaction Histories and Socio-Demographic Characteristics for Customer Churn

  • Fatih Cil Finansbank A.Ş.
  • Tahsin Cetinyokus Gazi University
  • Hadi Gokcen Gazi University
Keywords: Data mining, Customer churn, Decision trees and classification rules, Mutual funds


The need of turning huge amounts of data into useful information indicates the importance of data mining. Thanks to latest improvement in information technologies, storing huge data in computer systems becomes easier. Thus, “knowledge discovery” concept becomes more important. Data mining is the process of finding hidden and unknown patterns in huge amounts of data. It has a wide application area such as marketing, banking and finance, medicine and manufacturing. One of the most commonly used application areas of data mining is recognizing customer churn. Data mining is used to obtain behavior of churned customers by analyzing their previous transactions. In the same manner using with obtained tendency, other active customers are held in the system. It is possible to make by various marketing and customer retention activities. In this paper, it is aimed to recognize the churned customers of a bank who closed their saving accounts and determine common socio-demographic characteristics of these customers.


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Author Biographies

Fatih Cil, Finansbank A.Ş.
Vice President, Operations Business Development at Finansbank İstanbul
Tahsin Cetinyokus, Gazi University
Faculty of Engineering, Department of Industrial Engineering
Hadi Gokcen, Gazi University
Faculty of Engineering, Department of Industrial Engineering


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How to Cite
F. Cil, T. Cetinyokus, and H. Gokcen, “Knowledge Discovery On Investment Fund Transaction Histories and Socio-Demographic Characteristics for Customer Churn”, IJISAE, vol. 6, no. 4, pp. 262-270, Dec. 2018.
Research Article