Comparative analysis of the use of artificial intelligence and machine learning in the digital interaction of banks with customers in the context of sanctions

Abstract

The article provides a comparative analysis of the possibilities of using artificial intelligence (AI) and machine learning (MO) in the digital interaction of banks with customers in the context of increasing sanctions. The structure of the study follows the IMRAD model (Introduction, Methodology, Results, Discussion). Under the conditions of sanctions pressure, the banking industry is forced to adapt to new economic realities. AI and MO play a key role in optimizing customer interaction, reducing operational risks and improving cybersecurity. The purpose of this article is to analyze the application of these technologies in the banking sector, with an emphasis on the unique challenges that arise under the sanctions regime. The study uses methods of comparative analysis, a systematic review of financial practices, as well as quantitative and qualitative assessments of the effectiveness of AI and MO technologies in the banking sector. The sources of information were open analytical reports and digital platforms of Russian and international banks, as well as scientific literature on the topic. The analysis showed that AI significantly helps banks to provide a personalized approach to customer service by dynamically generating offers, as well as processing huge amounts of data in real time. MO allows you to automate the processes of assessing creditworthiness, preventing fraud and detecting suspicious transactions. Under sanctions, banks are more actively implementing these technologies to minimize dependence on external financial software providers. Restrictions provoke accelerated implementation of AI and MO, but create many problems related to access to advanced technologies and the development of import substitution. Banks that have successfully integrated these technologies demonstrate higher survival rates in the current environment. Thus, AI and MO have a significant impact on banks' adaptation to new economic and technological challenges.