A Micro-ethnographic Study of Big Data-Based Innovation in the Financial Services Sector: Governance, Ethics and Organisational Practices

Abstract

Our study considers the governance, ethics and operational challenges associated with the acquisition, manipulation and commodification of 'big data' in the financial services sector. To the best of our knowledge, there are no published studies describing empirical research undertaken within companies in this sector to understand how they are responding to such challenges: our field-based research is a significant initial contribution in this respect. We describe the results of a microethnographic study undertaken in a small-to-medium-sized company developing disruptive, technology-related platforms and services in the banking and retail sectors based on big data and associated analytics: these are used to derive commercially valuable insights from personal customer data in exchange for cash back and targeted rewards. The company was found to employ a multi-level innovation governance approach, underpinned by an ethical strategy based on a principle of mutual benefit (among stakeholders). Opt-in and informed consent for using data for specific purposes was supported by principles of data minimisation and anonymisation, with unrestricted use of secondary, anonymised and aggregated data to develop insights. Governance, which included contextual data protection legislation, payment-card industry data usage standards and internal corporate controls, presented as bespoke organisational practices relating to data security and privacy. These in total set the governance and ethics frame for big data innovation at the company within which it has had to be both adaptive and responsive under conditions of normative and regulatory uncertainty.

Artificial intelligence based decision-making in accounting and auditing: ethical challenges and normative thinking

“…What is more, Ananny and Crawford (2018) see that even algorithm designers often cannot explain how a complex system works in practice or which parts of the algorithm are vital for its operation. They also add that the more an individual knows about a system's inner processes, the more he/she should be held accountable, similar to remarks by Arthur and Owen (2019). Ananny and Crawford (2018) further suggest that people must hold systems accountable by examining them, rather than privileging a type of accountability that needs to check inside systems.…”

“…(p. 60) Privacy (and data protection) Privacy and related data protection problems were found to be another key challenge associated with adopting AI-based decision-making in an accounting setting (Martin, 2019a;West, 2019;Wright and Xie, 2017). Privacy is one of the most salient drivers of ethical concerns due to the rapid and largely unregulated increase in Big Data for use in AI-based systems (Arthur and Owen, 2019;Shilton and Greene, 2017). As AI evolves and chooses the sources of its data autonomously, its use of personal information is achieving a new level of power and speed that may not easily be comprehendible by users nor transparent.…”

Ethical Considerations of Service Organizations in the Information Age

“…These practices may result in the infringement of the individuals' privacy and human rights; if they did not voluntarily consent to share their personal information with the data collector (Belanche et al, 2024). Arthur and Owen (2022) report that that there is scope for financial services enterprises to employ a multi-level governance approach, standards and controls that are underpinned by an ethical strategy. These authors recommend that service practitioners ought to clarify to their customers that they are expected to opt-in or to opt-out from their terms and conditions about the use of their data.…”