Digital Transformation of the Banking Sector Based on Neuro-Multiagency Systems for Sustainable Development of the Data Economy

This article examines theoretical aspects related to the digital transformation of the banking sector through the use of neuro-multiagent systems to ensure sustainable development. The relevance of this study lies in the widespread use of artificial intelligence (AI) and multi-agent systems across all sectors, including business, fi nance, and banking, during the implementation of the National Project "Data Economy and Digital Transformation of the State", which largely creates certain prerequisites for the sustainable development of the banking system and the data economy. The novelty of this study lies in the development of a multi-agent system that enables the collection and processing of information reflecting the activities of commercial banks, generating data suitable for subsequent use, in particular, for identifying patterns for sustainable development. The aim of the study is to develop an artificial intelligence algorithm based on the n8n service as a multi-agent system that will enable the creation of a dataset for calculating normalized fi nancial stability ratios for commercial banks. To achieve this goal, the following tasks were set and solved: identify development trends and potential applications of Large Language Models (LLMs), which are large language models implemented on the n8n service within a multi-agent system; develop a design and build a working prototype of an AI bot in n8n for processing data in the cloud service; and calculate normalized fi nancial stability ratios for commercial banks based on the collected data.