Economic Bulletin of the National Mining University

 

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Issue:2025 №4 (92)
Section:Finances, accounting and taxation
UDK:336.7:004.01/08
DOI:https://doi.org/10.33271/ebdut/92.083
Article language:English
Pages:83-90
Title:Forecast of the use of artificial intelligence in banking and risk management
Author:Bezshtanko D. V., JSC «Kredobank»
Annotation:Methods. This work is based on the application of a number of scientific tools and methods, for example, scenario analysis in identifying examples of employee-artificial intelligence interaction. Induction for building forecasts based on typical stages of introducing new technologies into society; drawing analogies with historical trends in the development of the banking market, for example, automated banking systems of the late 20th – early 21st centuries, the introduction of CRM systems in banking management or the transition to a «digital state» in Ukraine. The above examples have the same stages of implementation from the idea to full acceptance and application by the banking community. Novelty. The author proposes to divide the implementing artificial intelligence into 4 stages and outlines the main features (technological, methodological and legal) of the application of artificial intelligence, as well as its application according to the selected periodization in management. Results. The use of artificial intelligence by banks has already begun. Understanding the stages of using artificial intelligence will allow building a further strategy for the development of banking, considering artificial intelligence, changing the policy of personnel management, processes, risks, and bank security. Thus, the first stage (by 2028) involves the completion of the initial stage of artificial intelligence implementation with the mass use of generative tools, online risk and process monitoring, and the first attempts by regulators to establish requirements for artificial intelligence in banking. The second stage (by 2030) involves strengthening regulation and integrating artificial intelligence into most banking processes, including risk management. The third stage – by 2035 – involves the transfer of a significant part of the functions currently performed by a bank employee to artificial intelligence with the subsequent reorganization of the system of banking products and processes. The fourth stage (by 2075) is a complete reformation of banks with the transfer of a significant list of artificial intelligence functions and increased risks associated with the work of artificial intelligence in the bank. The author also attempted to determine the change in the relationship between the bank employee and artificial intelligence and impose these changes on the proposed stages. The first stage (until 2030) involves the active use of artificial intelligence tools by the bank employee to solve his own tasks, respectively, that is why artificial intelligence is a tool. The second stage (until 2035) artificial intelligence acts as a partner and, accordingly, the employee indicates what exactly should be done and how, and artificial intelligence independently makes decisions on some tasks (typical, technical, etc.), without the participation of the employee. The third stage is a forecast for a period of up to 50 years - artificial intelligence makes decisions itself, and the employee only identifies the need. Practical value. The results of this study will allow, when implementing strategic planning, to consider artificial intelligence and its potential in one's own activities, including within the framework of risk management. 
Keywords:Artificial intelligence, Banking artificial intelligence, Risk management
File of the article:EV20254_083-090.pdf
Literature:
  • 1. Deloitte. (2023). The Future of Compliance: From policy to predictive AI. Retrieved from https://www2.deloitte.com
  • 2. European Central Bank. (2024). Exploring the Future of Banking in the Age of AI. ECB Working Paper Series. Retrieved from https://www.ecb.europa.eu
  • 3. MIT Technology Review. (2023). The Long View: Artificial Intelligence in Finance by 2050. Retrieved from https://www.technologyreview.com
  • 4. Pavlyuchenko, D.M. (2024). Vplyv shtuchnoho intelektu ta mashynnoho navchannia na bankivski posluhy. Akademichni Viziyi, (32), pp. 1-14. Retrieved from https://zenodo.org/records/12936652. https://doi.org/10.5281/zenodo
  • 5. Solodkyy, V.V., & Polishchuk, Yu.A. (2023). Vprovadzhennia shtuchnoho intelektu v ukrainskykh bankakh ta biznesi: perspektyvy ta zasterezhennia Finansy, oblik ta opodatkuvannia, 2(82), pp. 119-127. https://doi.org/10.33271/ebdut/82.119
  • 6. Bezshtanko, D. (2025). Ai and Bank’s Operational Risk Management. Three Seas Economic Journal, 6(2), 22-27. Retrieved from http://www.baltijapublishing.lv/index.php/threeseas/article/view/2922. https://doi.org/10.30525/2661-5150/2025-2-4
  • 7. Antifragile: Things that Gain from Disorder, 2013, 546p.
  • 8. Stratehia rozvytku fintekhu v Ukraini do 2025 roku. Natsionalnyy bank Ukrayiny [Fintech Development Strategy and Open Banking Roadmap National Bank of Ukraine]. Retrieved from https://bank.gov.ua [in Ukraine]
  • 9. Statista. (2024). Global AI in Banking Market Size 2018-2025. Retrieved from https://www.statista.com
  • 10. McKinsey & Company. (2023). The State of AI in Risk Management: Global Banking Insights. Retrieved from https://www.mckinsey.com
  • 11. Raiffeisen Bank International. (2023). AI Lab: Use Cases in Digital Banking. Retrieved from https://www.rbinternational.com
  • 12. JPMorgan Chase & Co. (2023). AI at JPMorgan: Risk, legal and compliance use cases. Internal Publication Summary.
  • 13. PwC. (2024). AI 2040+: Navigating the Risks and Opportunities in Financial Services. Pricewa- terhouseCoopers. Retrieved from https://www.pwc.com
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