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Digitalisation and Big Data Mining in Banking

1
Research Institute of Energy Management and Planning, University of Tehran, Tehran 1417466191, Iran
2
Faculty of Business and Law, De Montfort University, Leicester LE1 9BH, UK
3
Fashion Business School, London College of Fashion, University of the Arts London, London WC1V 7EY, UK
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2018, 2(3), 18; https://doi.org/10.3390/bdcc2030018
Received: 27 June 2018 / Revised: 12 July 2018 / Accepted: 17 July 2018 / Published: 20 July 2018
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. View Full-Text
Keywords: big data analytics; data mining; banking; survey big data analytics; data mining; banking; survey
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Hassani, H.; Huang, X.; Silva, E. Digitalisation and Big Data Mining in Banking. Big Data Cogn. Comput. 2018, 2, 18.

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