BANKACILIK SEKTÖRÜNDE VERİ MADENCİLİĞİNE YÖNELİK KURAMSAL BİR DEĞERLENDİRME

Abstract

Data mining is the process of finding relevant and meaningful information from large data piles and discovering useful information from data groups. The purpose of this research is to theoretically examine data mining applications in the banking sector. The data warehouse of the banking sector is growing day by day and banks need meaningful relationships between data for effective management. Data mining offers a structure that responds to this need of banks. Data mining uses many models and techniques while responding to these needs of banks. In the researches, it has been determined that the most used data mining models are classification and regression, association rules and clustering. The results obtained from the research show that as a result of using these models, data mining can be effectively used in customer relationship management, risk management, marketing activities and fraud detection and prevention of banks. As a result of data mining, it is possible for banks to increase their financial performance and grow by effectively managing the mentioned areas and making strategic decisions. The research contributes to the literature by presenting a theoretical approach and analysis.