AI AND BANK’S OPERATIONAL RISK MANAGEMENT

Abstract

In modern conditions, the use of AI is an advantage for business. This allows you to free up additional human resources and direct them to other tasks, speed up operations, and move to new areas of development. At the same time, the use of AI leads to an increase in risks, cyber threats, costs, and the possibilities of minimizing risk using AI determine the relevance of this study. In this scientific work, the subject of research is the process of applying and using AI in the banking business and in the banking risk management. The topics specified for the study were applied standard scientific methods of analysis, synthesis, deduction, and induction, it is possible to determine the directions of using AI in the banking sector and identify the possibilities of using AI together with risk management tools. The main goal of the research is to identify the advantages and threats of using AI in the banking sector. Taking into account the clear regulation of banks' activities, the paper highlights risk management tools: creation and maintenance of a database of internal and external operational risk events, key operational risk indicators, operational risk self-assessment, scenario analysis, mathematical modelling, analysis of process maps, comparative analysis. Today, almost all bank operations can be performed based on AI or systems that use it. Examples of where such tools are used include financial monitoring, active or treasury operations, securitization, Chat-bots and social networks, remote identification and office management. At the same time, AI systems are also used to control the risks arising in these processes: for process control and risk management. Each of the above risk management tools can be based on AI systems, which opens up significant opportunities for control, risk minimization and the release of human resources. However, one should not forget about potential threats that can worsen the quality of risk management: lack of specialists, data fragmentation in banks, validation problems, the cost of connecting AI, imperfection of existing models, and the possibility of data loss. In conclusion, it is worth noting that the use of artificial intelligence, along with significant advantages for banks, generates numerous risks that can be minimized with the use of the same artificial intelligence. This situation indicates a transition to a new stage of banking risk management – the stage of cooperation with artificial intelligence.