Central banks must prepare for Artificial Intelligence’s “profound” impact on the economy and financial system, the Bank for International Settlements (BIS) says in its recent Annual Economic Report. The central banking umbrella group contends that central banks need to urgently “raise their game” and proactively tackle the challenges and opportunities that AI presents.
“AI will affect financial systems as well as productivity, consumption, investment and labour markets, which themselves have direct effects on price and financial stability,” the BIS said in a special chapter of the report solely dedicated to AI, the economy, and implications for central banks.
“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,” reads an excerpt from the report.
Hyun Song Shin, Head of Research and Economic Adviser at the BIS, noted: “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.”
According to Cecilia Skingsley, Head of the BIS Innovation Hub, central banks are well positioned to capitalize on AI due to their early adoption of machine learning – which is what enables AI systems to imitate the way humans learn. “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,” she said.
AI Can Reduce Risk Of Repeating Past Failures
The BIS lists “nowcasting” as one of the potentially revolutionary use cases for AI in central banking. Nowcasting is essentially the use of real-time data, as opposed to historical data, to better predict inflation and other economics. A better read on inflation could help central bankers to avoid repeating some of the policy failures witnessed in the recent past.
Following the COVID-19 pandemic and Russia’s invasion of Ukraine, major central banks—including the U.S. The Federal Reserve and the European Central Bank—failed to accurately assess the magnitude of the global inflation surge.
Similarly, some economists have strongly argued that the unprecedented wave of monetary stimulus that central banks unleashed in response to Covid helped fuel the inflation and the cost of living crisis that followed. AI can help reduce these sorts of mistakes from recurring, assuming nowcasting is adopted and works as promised.
AI Won’t Set Interest Rates Or Replace Humans
While AI could help central banks enhance their forecasting capabilities and predict inflation more accurately, humans will still remain in control when it comes to the sacrosanct task of interest rate setting. For all its potential benefits, AI is still untested in regulated market environments and prone to errors. This means it cannot be relied upon to set interest rates, argues Skingsley.
“We like to hold humans accountable,” the former Swedish central banker said, referring to the crucial role borrowing costs play in society and the need for human judgement. “So I can’t really see a future where an AI will be setting rates.”
The BIS stressed there were limits to the extent to which AI could replace humans in central banks. “While it may be able to perform tasks that require moderate cognitive abilities and even develop ‘emergent’ capabilities, it is not yet able to perform tasks that require logical reasoning and judgement,” it said.
This notwithstanding, proactively exploring the possibilities that AI could unlock is imperative for policymakers. Central banks are not 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 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 notes.
Weighing The Opportunities And Risks
In the broader financial sector, AI can improve efficiencies and lower costs for payments, lending, insurance and asset management, the report said. Other uses include detecting fraud and money laundering.
The BIS, however, cautioned that AI also introduces risks, such as new types of cyber attacks. It cited the example of “data poisoning attacks” which occur when AI models are corrupted by cyber attackers, leaving them vulnerable to manipulation. “Data poisoning attacks refer to malicious tampering with the data an AI model is trained on. For example, an attacker could adjust input data so that the AI model fails to detect phishing emails,” it said.
Moreover, the rapid advancement of AI raises concerns around issues like bias, data privacy and overreliance on a few dominant providers of the AI models. “Any failure among or cyber attack on these providers, or their models, poses risks to financial institutions relying on them.”
AI could also pose financial stability risks when market participants rely on the same handful of algorithms to make decisions or execute transactions. “The behaviour of financial institutions using the same algorithms could amplify procyclicality and market volatility by exacerbating herding, liquidity hoarding, runs and fire sales.”
Strengthening Cooperation Among Central Banks
Ultimately, the successful integration of AI into the financial system will require central banks around the world to step up cooperation. “The call for action to central banks is to foster a community of practice,” Shin said. “To share experience, to share best practice, but also to share data and the models themselves.”
Such a community would foster the development of common standards, the BIS notes. “Central banks have a history of successful collaboration to overcome new challenges. The emergence of AI has hastened the need for cooperation in the field of data and data governance.”
While AI adoption has grown broadly, its integration in many sectors of the economy is still in the early phases, meaning there is a fair amount of time for central banks to test their models and research the technology more extensively.
A recent international survey by McKinsey revealed that almost three-quarters of organisations had adopted AI for one or more business functions, and around two-thirds of them are using generative AI. Nevertheless, just 8% reported using AI for five or more business functions. This “suggests that we are still in the initial stages of AI integration,” said Piero Cipollone, Member of the Executive Board of the European Central Bank (ECB), in a keynote address on the central bank’s views on AI at a conference in Rome in early July.
While optimistic about its prospects, Cippollone acknowledged the limitations of AI – specifically its inability to self-reflect like humans. “A key strength of human intelligence is the ability to reflect on its limits…But AI does not have this capacity for self-reflection. Nor does it have the ability to produce its own safeguards independently of human critical thinking.”
Author: Acutel
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