BIS: Central Banks Need ‘Comprehensive Risk Management Strategy’ for AI

Central banks must balance innovation and risk mitigation when looking to adopt artificial intelligence, the Bank for International Settlements (BIS) said in a report released Wednesday (Jan. 29).

“The report argues that central banks need to find a balance between fostering innovation using AI and mitigating the different risks that this technology may generate,” the report said. “Good governance schemes for the adoption of AI in the organizations, with a holistic view beyond technology, might help to achieve such a balance.”

The report, “Governance of AI adoption in central banks,” said AI presents central banks with both “huge opportunities” and complex risk management challenges.

The technology’s use cases include data analysis, research, economic forecasting, payments, supervision, banknote production and other critical functions of a central bank, according to the report.

At the same time, AI presents risks that include data security and confidentiality, “hallucinations” and reputational risks, per the report.

“A comprehensive risk management strategy can leverage existing risk management models and processes, in particular the well-established three lines of defense model,” the report said. “In incorporating the specific issues around AI and its use cases, risk managers at central banks can make use of the frameworks proposed by a number of international bodies. A good governance framework is key for adopting AI.”

During a June speech at the BIS annual meeting, Hyun Song Shin, economic adviser and head of the monetary and economic department at BIS, said AI could enhance central banks’ ability to monitor economic trends and detect financial crimes while potentially amplifying market volatility and cyber threats.

AI “has taken the world by storm and set off a golf rush across the economy, with an unprecedented pace of adoption and investment in the technology,” Shin said.

The BIS portfolio includes projects that use AI, Cecilia Skingsley, head of the BIS Innovation Hub, said in a June press release. These projects include Project Aurora, which uses payments data to detect money laundering, and Project Raven, which uses AI to enhance cyber resilience.

“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,” Skingsley said in the release.

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