EVOLUTION OF DIGITAL SOLUTIONS IN BANKS NON-PERMORMING ASSETS MANAGEMENT

Abstract

The article is devoted to examining the processes of digital transformation within the banking system and the impact of artificial intelligence implementation on the evolution of the banking industry. The study is based on analysis of historical data, a synthesis of scientific sources, statistical indicators and case studies of leading banks and fintech companies, as well as statistical data of the National Bank of Ukraine. The purpose of the research is to provide a comprehensive assessment of the digital transformation processes in the banking sector, to evaluate the role and economic feasibility of implementing AI technologies, and to identify the key opportunities and risks arising from this evolution. The methodological framework of the study includes a systems-based approach, comparative analysis, and retrospective review. In particular, comparative analysis is applied to contrast the traditional legacy-based banking model with AI-driven approaches across five core areas of banking operations. To establish an understanding of the technological foundations of transformation, conceptual analysis is used to examine key AI categories, including machine learning, deep learning, natural language processing (NLP), and big data. Statistical and economic analysis is employed to assess the impact of digital solutions, such as the decreasing the branch network and growing volume of transactions executed via digital payment instruments (including stablecoins) on operational efficiency and the competitive landscape. Using the examples of Ukrainian digital platforms such as SETAM, Prozorro Sale, and WEAGROBANK, the case-study method is applied to illustrate national specifics of digitalization. The findings indicate that digitalization and the deployment of advanced AI technologies provide traditional banks with substantial competitive advantages, enabling a shift from reactive to proactive and predictive risk management and business decision-making. The use of RPA and NLP-based chatbots significantly optimizes internal processes and customer support, resulting in cost savings related to personnel and physical branch maintenance.

The study determines that cloud computing constitutes the critical infrastructural foundation for scaling AI-driven solutions, transforming computing power into a utility-like service and reducing energy consumption by 80-90%. The research also identifies intensifying competition from digital payment instruments offering near-instant and low-cost cross-border transactions. The author sees prospects for further research in analysing the regulatory challenges associated with large-scale AI implementation in critical banking processes, with the aim of developing recommendations for banking institutions, as well as in exploring the potential synergies between traditional banks and fintech companies through open banking models, API integration, and open-banking ecosystems.