Abstract
Banking as a data intensive subject has been progressing continuously under the promoting influences of the era of big data. Exploring the advanced big data analytic tools like Data Mining (DM) techniques is key for the banking sector, which aims to reveal valuable information from the overwhelming volume of data and achieve better strategic management and customer satisfaction. In order to provide sound direction for the future research and development, a comprehensive and most up to date review of the current research status of DM in banking will be extremely beneficial. Since existing reviews only cover the applications until 2013, this paper aims to fill this research gap and presents the significant progressions and most recent DM implementations in banking post 2013. By collecting and analyzing the trends of research focus, data resources, technological aids, and data analytical tools, this paper contributes to bringing valuable insights with regard to the future developments of both DM and the banking sector along with a comprehensive one stop reference table. Moreover, we identify the key obstacles and present a summary for all interested parties that are facing the challenges of big data.
Highlights
The era of big data came along with both big opportunities and challenges, almost all science subjects are experiencing overflowing information at unpredictable volume and speeds [1]
The search process follows the usual approach by defining a range of keywords, where we employed the significant terms for both banking and Data Mining (DM) techniques, including banking, fraud detection, credit card, credit scoring, risk management, deposit, mortgage, debit, loan, customer relationship management (CRM), bank marketing; and data mining, clustering, text mining, classification and other specific DM technique terms
This paper successfully captured and systematically reviewed nearly 100 DM applications in banking post 2013. It fulfills the literature gap and serves as a quick reference guide for recent DM implementations in banking. Having reviewed these recent publications, it can be concluded that the banking sector has adopted DM mainly for fraud detection, risk management and CRM
Summary
The era of big data came along with both big opportunities and challenges, almost all science subjects are experiencing overflowing information at unpredictable volume and speeds [1]. As a data intensive subject, banking has been a popular implementation field for researchers with DM skills over the past decades of the information science revolution. The development and popularization of e-banking and mobile banking adds to the exponential growth of real time banking information. These continuous developments and the rapidly increasing availability of big data make mastering relevant big data analytics tools one of the most crucial tasks for the banking sector
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