Elucidation of big data analytics in banking: a four-stage Delphi study

Mohammad Soltani Delgosha, Nastaran Hajiheydari, Sayed Mahmood Fahimi

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: In today's networked business environment, a huge amount of data is being generated and processed in different industries, which banking is amongst the most important ones. The aim of this study is to understand and prioritize strategic applications, main drivers, and key challenges of implementing big data analytics in banks.

Design/methodology/approach: To take advantage of experts' viewpoints, the authors designed and implemented a four-round Delphi study. Totally, 25 eligible experts have contributed to this survey in collecting and analyzing the data.

Findings: The results revealed that the most important applications of big data in banks are “fraud detection” and “credit risk analysis.” The main drivers to start big data endeavors are “decision-making enhancement” and “new product/service development,” and finally the focal challenge threatening the efforts and expected outputs is “information silos and unintegrated data.”

Originality/value: In addition to stepping forward in the literature, the findings advance our understanding of the main managerial issues of big data in a dynamic business environment, by proposing effective further actions for both scholars and decision-makers.
Original languageEnglish
Pages (from-to)1577-1596
Number of pages20
JournalJournal of Enterprise Information Management
Volume34
Issue number6
Early online date14 Sep 2020
DOIs
Publication statusPublished - 11 Nov 2021

Keywords

  • Big data analytics
  • Big data applications
  • Business value
  • Challenges
  • Banking industry

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