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.
