BIS urges central banks to use AI for financial stability

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The Bank of International Settlements (BIS) has released a report advocating for central banks to adopt artificial intelligence (AI) as a means to enhance financial stability.

Published on 25 June, the report highlights how central banks can harness the power of AI for various benefits. However, it also points out areas where caution is needed, explaining the potential implications of adopting this technology.

The adoption of AI has surged dramatically, impacting not only people’s daily lives, with data showing that ChatGPT alone reached one million users in less than a week and nearly half of US households have used gen AI tools in the past 12 months.

However, this BIS report shows that the technology can be used for more than language translation or film recommendations.

Hyun Song Shin, Head of Research and Economic Adviser at the BIS, said: “New generation AI models have captured our collective imagination through their uncanny abilities, but they also have a direct bearing on how central banks do their jobs. 

“Vast amounts of data could provide us with faster and richer information to detect patterns and latent risks in the economy and financial system. All this could help central banks predict and steer the economy better.”

Across four key sectors of finance – payments, lending, insurance and asset management – there are significant opportunities for AI to enhance processes and operations, explains the BIS.

AI models are playing a crucial role for financial institutions by analysing data patterns. For instance, they enhance fraud detection and identify security vulnerabilities. Globally, about 70% of financial services firms use AI to predict cash flow, manage liquidity effectively, refine credit scoring mechanisms and strengthen fraud detection capabilities.

AI is also helping improve central banks’ ability to predict inflation and other economic factors using real-time data. Additionally, AI helps them identify vulnerabilities in the financial system, which enhances risk management capabilities.

Notably, the report explores how AI can be ‘harnessed’ for policy objectives. It states that AI can improve how central banks understand the economy, make decisions about money, and monitor financial systems. This advancement is significant, yet it underscores the need to establish rules to protect people’s information and ensure responsible AI usage.

When discussing AI, conversations about data are inseparable. Understandably, data is the fuel on which the technology runs on, which is why BIS has asked central banks to come together to form a “community of practice” to share knowledge, data, best practices and AI tools.

The report suggests that establishing common data standards could simplify access to publicly available data and automate the collection of relevant information from different official sources. This would improve the training and effectiveness of machine learning models. 

Furthermore, it proposes the creation of specialised repositories to share open-source code for data tools, potentially starting with other central banks before wider public access. One such initiative is the BIS Open Tech platform, which fosters international collaboration and facilitates the exchange of statistical and financial software.

As the BIS’ primary goal is to foster international monetary and financial cooperation while serving as a bank for central banks, it is leveraging AI in various initiatives. This is being done through its Innovation Hub, which has already brought together central banks to collaborate on projects like Project Aurora.

This project uses synthetic data to simulate money laundering activities and compares different machine learning models, focusing on analysing payment relationships to enhance financial security measures.

Cecilia Skingsley, Head of the BIS Innovation Hub, commented: “Central banks were early adopters of machine learning and are therefore well positioned to make the most of AI’s ability to impose structure on vast troves of unstructured data.

“For example, Project Aurora explores how to detect money laundering activities from payments data and Project Raven uses AI to enhance cyber resilience, to mention just two from our portfolio.”