Abstract
One of the main sectors that makes heavy use of the development of advanced computational methods is the banking sector. The goals of our research are as follows: 1) to compare scientific and regulatory approaches to defining artificial intelligence (AI) and machine learning (ML), 2) to propose AI and ML definitions for regulatory purposes that allow us to clearly state if a given method is AI/ML or not, 3) to compare the complex quantitative methods applied in banking in terms of complexity and interpretability in order to provide a clear classification of methods to the interested parties (practitioners and management), 4) to propose a possible approach towards the further development of quantitative methods in the areas of required strict interpretability. Our literature review focuses on the definitions of AI/ML applied by scientists and regulators, as well as the proposals of application of complex quantitative solutions in different banking domains. We propose practical definitions of AI and ML based on the current state of the art and requirements of clarity in the banking industry (a very limited risk appetite regarding regulations non‑compliance) and compare quantitative methods applied in different banking domains. For regulatory purposes, we propose general and inclusive definitions of AI and ML which allow for a clear classification of specific methods. In the case of strict requirements towards the interpretability of applied methods, we propose a gradual and controlled increase in the complexity of existing solutions. Therefore, we propose the differentiation of quantitative methods in terms of interpretability and complexity. We also think that the definitions of AI/ML in further regulations should make it possible to clearly say whether particular approaches are AI/ML. Our research is directed to policymakers, practitioners, and executives related to the banking sector.
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