As artificial intelligence (AI) rapidly transitions from a nascent development to a ubiquitous technology accelerating advancements across the financial landscape, far-reaching implications for central banks worldwide are quickly emerging.

As stewards of monetary policy and financial stability, central banks need to grapple with AI’s legitimately game-changing potential while harnessing its capabilities to enhance their own operations.

During the Bank for International Settlements’ (BIS) Annual General Meeting at end-June 2024, the bank released its Annual Economic Report 2024, which highlights the transformative impact of AI on the financial sector in general, and central banking in particular.

This paradigm shift presents both opportunities and challenges for institutions like the Monetary Authority of Singapore (MAS) and other Asian central banks at the forefront of financial innovation.

AI’s Impact on Financial Systems

The BIS report underscores the remarkable speed at which AI, particularly generative AI powered by large language models, has penetrated the financial sector.

Unlike previous technological innovations that took years or decades to achieve widespread adoption, AI tools like ChatGPT reached millions of users within days. This rapid uptake extends across industries, with financial services firms leading the charge in AI integration.

The report elaborates that AI is poised to dramatically alter the financial sector, from payments and lending to insurance and asset management. In payments, AI-powered systems can enhance fraud detection and streamline cross-border transactions, potentially revitalising correspondent banking relationships that have dwindled due to regulatory pressures.

For lending, AI’s ability to analyse alternative data sources could improve credit scoring and expand financial inclusion, particularly beneficial in emerging Asian economies with large unbanked populations.

The insurance industry stands to benefit from AI’s prowess in risk assessment and claims processing, while asset managers can leverage AI for more sophisticated portfolio allocation and algorithmic trading.

However, the widespread adoption of AI also introduces new risks, such as increased cyber vulnerabilities and the potential for algorithmic collusion in financial markets. The report emphasises that AI’s impact on central banks is twofold: it influences their core activities as economic overseers and directly affects their operations through changes in the financial system.

Central Banks as AI Adopters

Central banks are not merely observers of this AI revolution; they are actively exploring ways to harness AI’s potential. The MAS, known for its forward-thinking approach, has been at the forefront of exploring integrating AI into its operations. AI can enhance central banks’ capabilities across various functions, including economic forecasting, financial stability monitoring, and regulatory compliance.

One promising application is in ‘nowcasting’ – using real-time data to assess current economic conditions. AI models can process vast amounts of unstructured data from diverse sources, providing central banks with more timely and granular insights into economic activity. This could be particularly valuable for Asian economies characterised by rapid change, and less formalised data collection systems.

AI also offers powerful tools for detecting patterns in complex financial data sets, potentially improving early warning systems for systemic risks. For instance, machine learning algorithms could help identify emerging vulnerabilities in the banking sector or spot anomalies in payment systems that may indicate fraudulent activity.

AI can streamline regulatory processes, enhancing the efficiency of know-your-customer (KYC) and anti-money laundering (AML) procedures. This could help address the decline in correspondent banking relationships, a concern highlighted in the BIS report.

The BIS further notes that central banks see significant potential in using AI to bolster cyber defences, automating threat detection and response mechanisms.

Challenges and Considerations

While the potential benefits are significant, central banks face several challenges in adopting AI. One key issue is the ‘black box’ nature of some AI models, which can make it difficult to explain decisions or predictions.

This lack of transparency could be problematic for central banks, which often need to justify their actions to the public and policymakers.

Data quality and availability present another hurdle. AI models require vast amounts of high-quality, timely data to function effectively. Central banks must balance the need for comprehensive data with privacy concerns and regulatory restrictions on data sharing.

There’s also the question of in-house development versus reliance on external providers. While using off-the-shelf AI solutions may be more cost-effective in the short term, it could create dependencies on a small number of foreign tech giants. This is a particular concern for Asian central banks seeking to maintain technological sovereignty.

Implications for Monetary Policy

AI’s impact extends beyond operational efficiencies to the very core of central banking: monetary policy. By providing more accurate and timely economic data, AI could help central banks make more informed policy decisions.

However, the BIS study cautions that it may also alter the transmission mechanisms of monetary policy in ways that are not yet fully understood.

For instance, AI-driven pricing algorithms used by firms could lead to faster and more uniform price adjustments in response to economic shocks. This could potentially make inflation more responsive to monetary policy actions, but might also introduce new sources of volatility.

Moreover, as AI reshapes labour markets and productivity, it could fundamentally alter the relationship between employment, wages, and inflation — key considerations for monetary policymaking. Central banks will need to adapt their analytical frameworks to account for these structural changes.

Embracing AI in Central Banking

The BIS report strongly advocates for increased collaboration among central banks to address the challenges posed by AI. It suggests the formation of a “community of practice” to share knowledge, data, best practices, and AI tools. This collaborative approach could help central banks, particularly those with limited resources, to leverage AI effectively while managing associated risks.

The BIS Innovation Hub, with centres in Singapore and Hong Kong, plays a vital role in fostering such cooperation. These hubs are exploring AI applications in areas like regulatory technology and green finance, sharing insights that benefit central banks globally.

For institutions like the Monetary Authority of Singapore (MAS) and other Asian central banks, the report’s findings underscore that developing a strong AI talent pool is essential. This may involve partnerships with universities, tech firms, and other central banks to build the necessary skills and knowledge base.

As AI continues to evolve, central banks must strike a delicate balance between embracing innovation and managing risks. They must also consider the broader societal implications of AI, such as its potential impact on financial inclusion and inequality.

AI represents both a powerful tool and a disruptive force for central banks, and the report makes clear that for central banks, embracing AI is not just an option, but a necessity in maintaining their effectiveness as guardians of monetary and financial stability.

Institutions like the MAS that can effectively navigate this new landscape – leveraging AI into their operations and policy frameworks – will be well-positioned to shape the future of central banking in the digital age.

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