Central banks have a responsibility to safeguard the financial stability of their economies, but they should also be at the forefront of emerging technologies. To handle the challenges of artificial intelligence, for example, central banks must anticipate its macroeconomic implications and integrate it into their own operations.
According to a new report from the Bank for International Settlements (BIS), most central banks are behind the curve in both respects. The slow adoption of AI could hinder these institutions’ ability to quickly adapt to economic shifts driven by AI itself.
“There is an urgent need for central banks to raise their game,” BIS wrote. “To address the new challenges, central banks need to upgrade their capabilities both as informed observers of the effects of technological advancements as well as users of the technology itself.”
Improving Infrastructure
Embracing AI will require many central banks to invest in costly infrastructure and hire specialized staff or outsource artificial intelligence services to a third party. While an external model will be cost-effective, it could also make central banks too dependent on a few third-party providers.
The European Central Bank recently voiced its concerns about the concentration of AI services in Europe’s financial systems. The ECB warned that this reliance could potentially lead to a herd mentality among financial institutions, and even cause systematic distortions in the economy.
The BIS report echoed the ECB’s concerns, and reiterated AI’s potential for bias. AI’s flaws only highlight central banks’ need to have the proper infrastructure and staffing. An optimal infrastructure also protects those financial institutions against emerging fraud trends, which often leverage AI themselves.
A Community of Practice
While some infrastructure improvements might be unavoidable, BIS concluded that central banks might be better off cooperating with each other, pooling their resources, and identifying synergies.
This includes creating common data standards for easier information sharing between banks and repositories to house the open source code of data tools. BIS, which acts as an umbrella organization for central banks, even suggested that banks share AI models that have been successful in financial applications.
“To harness the benefits of AI, collaboration and the sharing of experiences emerge as key avenues for central banks to mitigate these trade-offs, in particular by reducing the demands on information technology infrastructure and human capital,” BIS noted. “Central banks need to come together to form a ‘community of practice’ to share knowledge, data, best practices, and AI tools.”
