In the last decade, the presence of computational resources and data availability for general users, both on and off the Internet, has contributed to the development and application of artificial intelligence (AI) techniques in business and industry.
Machine learning is an application of artificial intelligence that holds the belief that data-driven systems can learn, recognise patterns, and draw conclusions on their own without the need for human guidance. The decision-making process begins with data-gathering and processing. instead of hand-coded set-specific instructions, a huge amount of data and algorithms can be implemented to perform a specific task.
Bank digitalization refers to the adoption of all available digital technologies with the twofold objective of contributing to a general increase in customers' standard of living and enhancing business performance. Today, digital has a significant impact on not only the customer-bank relationship, but also on the banking model as a whole.
To the best of our knowledge, this is the first field research paper based on questionnaires aimed at teasing out the prospected benefits and risks associated with digitalization (and more specifically in machine learning) in banking as identified by practitioners and professional experts operating in this area. We will investigate the most promising areas of digitalization and machine learning applications also in terms of improvement in the performance of bank risk management.
The goal of this research is to help both authorities and practitioners better assess the current stage of digitalization in banks and frame out all the possible benefits and shortcomings resulting from its application. Our contribution will take the form of a highly-innovative, evidence-based paper addressed to both supervisors and practitioners, establishing academics as a point of contact between them.
Digital transformation is expected to affect the nature of existing risks and frequently introduce new, possibly unexpected, risks.
To the best of our knowledge, this is the first field research paper based on questionnaires aimed at teasing out the prospected benefits and risks associated with digitalization (and more specifically in machine learning) in banking as identified by practitioners and professional experts operating in this area. We will investigate the most promising areas of digitalization and machine learning applications also in terms of improvement in the performance of bank risk management.
Indeed, this is one of the key topics in the agenda of policy makers and bankers. In particular the ECB is carefully overseeing these innovations: "I assure you that ECB supervisors will be keeping a close eye on all of these matters in the coming years. Through our regular Supervisory Review and Evaluation Process (SREP) and on-site inspection methodologies we are increasingly integrating the most up to date concerns that arise from the move to digitalisation. Over time, these themes will become more and more central to the way we supervise and test banks governance, risk management, and IT architecture." (The digitalisation of banking - supervisory implications
Speech by Pentti Hakkarainen, Member of the Supervisory Board of the ECB, at the Lisbon Research Centre on Regulation and Supervision of the Financial Sector Conference, Lisbon, 6 June 2018).
As also pointed out by the European Banking Authority, "data management by financial institutions as one of the key pillars for the development, implementation and adoption of advanced analytics (including AI). This pillar should be supported by high governance standards and additional `trust elements if new technologies around AI and machine learning are to be used. Some of these `trust elements should help answer concerns on: ethics, fairness and the avoidance of bias; the explain ability and interpretability of the outcomes; and security." (Digital finance: Towards a common EU approach, Speech by Jose Manuel Campa, 3 March 2020)
That said, we would expect that findings of this research may help both authorities and practitioners to better assess the current stage of digitalization in banks and frame out all the possible benefits and shortcomings resulting from its application. Our contribution will take the form of a highly-innovative, evidence-based paper addressed to both supervisors and practitioners, establishing academics as a point of contact between them.