Changing Bank Analysis Processes under the Influence of Artificial Intelligence Technologies

The given article explores the areas of application of artificial intelligence (AI) focused on the transition from traditional periodic and expert methods of analysing bank activity to integrated autonomous data processing systems.

The objective of the study is to give a strong rational for such a transition, taking into account the limitations of existing methods, including the fragmentary nature of procedures, a high degree of dependence on expert interpretation and time delays inherent in decision-making.

The methodological basis of the study becomes the analysis of the institutional and functional characteristics of the traditional and integrated analytical cycles, the comparison of various methods of data interpretation, and the study of practical capabilities of modern automation technologies.

The results indicate that the development of integrated analysis transforms its role from a supporting instrument to an integral ingredient of the digital banking process. The research presents a novel approach used in banking activities, the so-called «self-assessment», in which the processes of collecting, processing and interpreting information are automated and integrated directly into the infrastructure of execution of operations.

The practical significance of the study lies in the results, which can be used to develop and implement AI-based embedded analysis systems in credit institutions. This allows them to transition from traditional methods with periodic data processing to continuous operation, which helps reducing operational risks and strengthen financial stability in compliance with ethical and legal requirements for the use of AI technologies.

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