The global body representing a collective of central banks has said it is testing an AI tool to collect data to assess climate-related financial risks.
The Bank for International Settlements (BIS), the Bank of Spain, Germany’s Bundesbank, and the European Central Bank, said on Tuesday (19 March) that its experimental Gaia AI project was able to analyse company disclosures on emissions, voluntary net-zero commitments, and green bond insurance.
Bank regulators and insurers need to use large amounts of data to assess the impact of climate change on financial institutions effectively. The central banks said a lack of global reporting standards makes this a complicated task.
Traditionally, the procedure involves reviewing large amounts of different document formats, including text, tables, and charts.
Each key indicator for climate-related financial risk requires an analyst to manually contact the institution or check if it has been made public.
The BIS said Gaia AI makes comparing key indicators of climate-related financial risks quicker and easier. With Gaia AI, analysts can extract and analyse multiple key indicators from several companies at once.
“Project Gaia showcased the potential of generative AI to streamline central bank operations,” Raphael Auer, head of the eurosystem centre, at the BIS Innovation Hub said.
“By automating and refining data processes, GenAI can change the way we work, offering a blueprint for efficient, data-driven progress,” he added.
Under new US and EU rules, listed companies face new mandatory climate-related disclosures. This means that more detailed information will be required, rather than traditional voluntary approaches.
The BIS said Gaia AI will become publicly available as a web-based service for analysts.
“Gaia has broken new ground by integrating LLMs into an application and using it for the extraction of data on climate-related financial risks,” the BIS and central banks said in a joint statement. “The flexible design may serve as a model for AI-enabled applications in a broader range of use cases for central banks and the financial sector.”
