THE USE OF ARTIFICIAL INTELLIGENCE IN CENTRAL BANK ACTIVITIES: FOCUS ON SUPTECH / REGTECH AND GREEN FINANCE

Abstract

Central banks play a crucial role in ensuring financial stability and supporting sustainable development, yet they increasingly face the challenges posed by digital transformation and the growing complexity of financial markets. This study explores the integration of artificial intelligence (AI) into the operations of central banks, with a particular focus on supervisory technologies (SupTech), regulatory technologies (RegTech), and green finance. The methodological basis of the analysis consists of case studies of selected BIS Innovation Hub projects—Ellipse, Aurora, Gaia, and Symbiosis—which demonstrate the potential of AI-driven tools for enhancing regulatory oversight, combating money laundering, and collecting climate-related data from corporate reports. The findings reveal that AI enables more adaptive, efficient, and real-time decision-making processes, transforming traditional supervisory frameworks into proactive, data-intensive systems. For example, project Ellipse illustrates how natural language processing and machine learning can be used to extract meaningful insights from both structured and unstructured data; project Aurora shows the use of privacy-enhancing technologies for cross-border anti-money laundering collaboration; project Gaia applies large language models to automate the extraction of climate-related metrics from financial disclosures; and project Symbiosis focuses on tracing Scope 3 emissions across global supply chains. While the practical benefits of these technologies are already evident, further research is needed to assess their scalability, regulatory implications, and infrastructure requirements. The study underscores the importance of cross-sectoral cooperation and legal adaptation in enabling a responsible and transparent use of AI in central banking