The rapid adoption of artificial intelligence (AI) in financial services demands a similar embrace by central banks, argues the Bank for International Settlements (BIS), with the global authority believing the technology will serve a critical role for central banks in regulating industry, improving monetary policy, and spurring economic growth.

The BIS, commonly known as the ‘central bank of central banks’, has taken a largely sanguine view of the emerging technology, urging policymakers to embrace AI to help “sharpen their own analytical tools in pursuit of financial and price stability”.

Central banks, the BIS wrote in a pre-release chapter of its Annual Economic Report 2024, cannot be simply “passive observers in monitoring the impact of AI on the economy and the financial system”.

“They can harness AI tools themselves in pursuit of their policy objectives and in addressing emerging challenges.

“In particular, the use of LLMs [large language models] and AI can support central banks’ key tasks of information collection and statistical compilation, macroeconomic and financial analysis to support monetary policy, supervision, oversight of payment systems and ensuring financial stability.”

The BIS argued that, as early adopters of machine learning tools, central banks are already well-positioned to reap the benefits of these technologies.

Among the most practicable use cases for AI among central banks include ‘nowcasting’, using real-time data to more accurately predict inflation and other economic variables and sift through data for financial system vulnerabilities.

“This method can significantly improve the accuracy and timeliness of economic predictions, particularly during periods of heightened market volatility.”

For instance, an LLM fine-tuned with financial news can readily extract information from social media posts or non-financial firms’ and banks’ financial statements or transcripts of earning reports and create a sentiment index.

“The index can then be used to nowcast financial conditions, monitor the build-up of risks or predict the probability of recessions.”

AI-based nowcasting could also be useful in understanding real-economy developments. For example, transaction-level data on household-to-firm or firm-to-firm payments, together with machine learning models, can improve nowcasting of consumption and investment.

According to the BIS, central banks can also use AI, together with human expertise, to better understand factors that contribute to inflation.

“Neural networks can handle more input variables compared with traditional econometric models, making it possible to work with detailed datasets rather than relying solely on aggregated data. They can further reflect intricate non-linear relationships, offering valuable insights during periods of rapidly changing inflation dynamics.”

AI could also play an important role in supporting financial stability analysis, the BIS added.

“The strongest suit of machine learning and AI methodologies is identifying patterns in a cross-section. As such, they can be particularly useful to identify and enhance the understanding of risks in a large sample of observations, helping identify the cross-section of risk across financial and non-financial firms.”

As well, pairing AI-based insights with human judgment could help support macroprudential regulation, the BIS wrote.

“When combined with rich data sets that provide sufficient scope to find patterns in the data, AI could help in building early warning indicators that alert supervisors to emerging pressure points known to be associated with system-wide risks.”

However, the use of AI is contingent not only on quality and timely datasets and “robust governance frameworks”, but also in structuring data “in a way that yields insights”.

“This… is where machine learning tools, and in particular LLMs, excel,” the BIS wrote.

“They can transform unstructured data from a variety of sources into structured form in real-time.

“Moreover, by converting time series data into tokens resembling textual sequences, LLMs can be applied to a wide array of time series forecasting tasks.

“Just as LLMs are trained to guess the next word in a sentence using a vast database of textual information, LLM-based forecasting models use similar techniques to estimate the next numerical observation in a statistical series.”

The BIS cautioned that central banks will also face important trade-offs in selecting either external or developing internal AI models, as well as in the choice of collecting and providing in-house data versus sourcing them from external providers.

“Using external models may be more cost-effective, at least in the short run, and leverages the comparative advantage of private sector companies. Yet reliance on external models comes with reduced transparency and exposes central banks to concerns about dependence on a few external providers.”

The choice of either external or internal models and data will also have “far-reaching implications for central banks’ investments and human capital”.

It added: “Together with the centrality of data, the rise of AI will require a rethink of central banks’ traditional roles as compilers, users and providers of data.”

In order to fully harness the benefits of AI and mitigate trade-offs, the BIS urged for greater collaboration and sharing of experiences with the technology, in particular by reducing the demands on information technology infrastructure and human capital.

“Collaboration can yield significant benefits and relax constraints on human capital and IT. For one, the pooling of resources and knowledge can lower demands among central banks and could ease the resource constraints on collecting, storing and analysing big data as well as developing algorithms and training models.”

Central bank collaboration and experience-sharing could also help in identifying areas in which AI adds the most value and where synergies can be leveraged.

“Common data standards could facilitate access to publicly available data and facilitate the automated collection of relevant data from various official sources, thereby enhancing the training and performance of machine learning models.

“Additionally, dedicated repositories could be set up to share the open source code of data tools, either with the broader public or, at least initially, only with other central banks.

The BIS called on central banks worldwide to “come together to form a ‘community of practice’ to share knowledge, data, best practices and AI tools”.