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

Abstract

PurposeIn 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/approachTo 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.FindingsThe 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/valueIn 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.

Exploring the paths to big data analytics implementation success in banking and financial service: an integrated approach

“…Investment in big data analytics (BDA) has been a crucial managerial decision for the banking and financial service (BFS) sector, not only due to its potential to create business value (Delgosha et al, 2020;Popovi c et al, 2018), but considering organizational changes and resource commitments that it might arise (Davenport and Harris, 2007). BDA can better be defined by considering key characteristics of big data, besides encompassing elements of tools, infrastructure and means of visualizing that ultimately generate managerial insights and add value to the decision-making process (Mikalef et al, 2018).…”

“…For instance, HSBC is planning to recruit 1,000 data scientists to improve customer experience and risk management with aid of BDA (LexisNexis, 2019). The investment in BDA has brought tremendous benefits to BFS firms (Cohen, 2018;Delgosha et al, 2020). JP Morgan Chase, for instance, has detected fraud risk among its customers by monitoring buying patterns and spending behaviours, while American Express offers their customers data-driven promotions (e.g.…”

“…Thus, BFS managers require a series of guidance on how their organizational conditions (e.g. business process, strategies and organizational routines) can best fit BDA implementation (Delgosha et al, 2020;Wang et al, 2020).…”

The Impact of Big Data on Banking Operations

“…Therefore, nancial fraud detection is essential to minimize the risk for the organization. There are many frauds in the banking and nancial services sectors, so the companies use improved and better fraud detection methods based on real-time analysis of big data (Amakobe, 2015;Delgosha et al, 2019). Banks and other nancial service companies use algorithms based on real-time transaction data to obtain more accurate and less intrusive fraud detection methods.…”

“…The application and development of big data is a general trend. However, premature investment in selecting software and hardware that are not suitable for the bank's speci c actual situation or too conservative inaction will hurt commercial banks' development (Balachandran & Prasad, 2017;Delgosha et al, 2019;Shamim et al, 2019).…”