Post by Bank for International Settlements – BIS

Central banks are increasingly exploring and using generative artificial intelligence (AI) to support their activities, such as economic and monetary analysis, statistical production, financial supervision and payment oversight. Large language models (LLMs) have unlocked unprecedented opportunities to deal with the textual data that central banks both use and produce. Continuous and rapid innovations are further expanding the capabilities and quality of generative AI’s outputs. Examples include retrieval-augmented generation techniques (RAGs), small language models (SLMs) and agentic AI. In practice, however, the use of generative AI is not without challenges, especially related to data quality and IT infrastructure. One question for central banks is how to make the most of these promising yet often embryonic technological advances in an efficient, effective, ethical and safe way. In its latest IFC Bulletin, the BIS’s Irving Fisher Committee on Central Bank Statistics (IFC) has identified three distinct areas of focus: •The design and implementation of governance frameworks for managing the associated risks and harnessing AI effectively and responsibly. •Technical aspects, reflecting the need to secure adequate IT resources as well as to continue supporting interoperable data processes, standards and systems. •The importance of international cooperation and knowledge exchange across central banks and with other stakeholders involved in the data ecosystem. Read more here: https://bit.ly/4m0axde