Analysing the implementation of the combination of ESG data, Big Data and AI within a financial institution, an explorative case study

Master thesis (2021)

Authors

M.P. Breeman Technology, Policy and Management

Contributors

J. Rezaei Transport and Logistics - TPM (graduation committee member)
N. Mouter Transport and Logistics - TPM (mentor)
Z. Roosenboom-Kwee Economics of Technology and Innovation - TPM (graduation committee member)
Faculty
Technology, Policy and Management, Technology, Policy and Management
Copyright: © 2021 Max Breeman
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Published Date

17-12-2021

Language

English

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Faculty

Technology, Policy and Management

Abstract

Over the last few years, regulations, changes in governance, and societal pressure have led to a push to rethink a firms’ approach to sustainability. This push created a need to place sustainability and numerous relevant technologies and approaches at the centre of the firms’ decision-making process. Within the financial industry, the combination of novel data technologies such as Big Data and Artificial Intelligence (AI) with the inclusion of sustainability, or so-called “ESG”, Environment, Social, and governance data spearpoint this new ‘sustainable’ frontier.
Literature shows that the implementation of the combination of ESG data, Big Data and AI, within a financial institution and as a corporate resource is a rather novel subject, with no directly related literature available. Thus, this thesis aims to address the topic of implementing ESG data, Big Data and AI within a financial institution. The main research question answered within this thesis is set out to explore this combination of sustainability data, also known as ESG data, Big Data and AI. This study aims to provide a starting point to fill this knowledge gap by creating novel theoretical propositions to be tested in future research. The following research question has been devised to address this research problem.

What observations can be extracted from assessing the introduction of a Big Data and AI toolset applying ESG data within a procedure?

The main research question is answered through the use of theoretical propositions. These novel theoretical propositions illustrate key observations made during the case study. For each proposition, future research directions are given. These propositions, thus, the answer to the main research question are:
- The perception within a firm of using Big Data and AI within a process could affect the learning rate and the learning approach taken by the user. This affects the acceptance of the technology. Thus, the perception could affect the adoption rate of Big Data and AI within a firm.
- If Big Data and AI are used within a process, people tend to be convinced by Big Data and AI used within the process, thus Big Data and AI can be used to convince people of the validity of the results of the process.
- If conferred management information is substantiated by an information process using Big Data and AI, then people do not have the tendency to acknowledge the inherent biases in such processes.
- If Big Data and AI are used within a process, data quality and source are perceived as of less importance.
- There could be causation between one's knowledge of Big Data and AI, and the perception of bias when assessing a process that uses Big Data and AI.
- ESG data is context-dependent, illustrating that a structured or unstructured approach to ESG data depends on the application of ESG data.