Central banks have already embraced AI to help them as the guardians and stewards of monetary policy and financial resilience. Many are leveraging next-level capabilities to boost real-time monitoring of economic indicators, automate and streamline processes, and support supervision of innovations in payments and lending systems, among other applications.
Yet, as Piero Cipollone—an executive board member of the European Central Bank—noted in a speech on how the ECB is using AI, it has not fundamentally altered the way central banks view monetary policy. Their core function will always remain to maintain price stability.
Instead, AI augments their ability and can remove many obstacles central banks face in carrying out that mandate.
For example, Dr. Ben Bernanke, the former chair of the US Federal Reserve, conducted an independent review of the Bank of England’s economic forecasting and found in his report that central banks as a whole performed poorly at macroeconomic forecasting in the face of global uncertainty and volatility. His first recommendation to address the challenge was rapid updating and modernization of software and technology, AI included.
It is becoming increasingly clear that central banks and their leadership need to build on their existing capabilities and ramp up AI investments and efforts. But it is equally obvious that they—and the broader industry—cannot be blind to the risks and limitations of the technology’s capabilities either.
Addressing the Challenges of AI in the Financial Sector
There are several challenges central banks must consider as they strengthen their investment in digital tools. These include:
- The rise of more sophisticated cyber-attacks and new ways to facilitate fraud or scams.
- Hallucinations that create inaccurate or nonsensical outputs.
- The spread of disinformation through deepfakes and repeating existing and emerging biases or inaccuracies, leading to unfair or incorrect decisions.
- The lack of transparency in AI models makes it difficult to identify potential errors.
- Privacy and ethical concerns, as well as a lack of accountability.
- Introducing herd behavior in financial markets by using the same set of algorithms – which could impact financial stability.
- An uncertain regulatory environment, with possibly conflicting laws and policies in different jurisdictions.
Many banking leaders already recognize that AI can help address its own shortcomings. AI can, for instance, detect and prevent incidents such as cybersecurity breaches, scams, and the like by identifying irregularities in users, systems, and networks.
Likewise, its ability to rapidly scan, process, and analyze vast amounts of information can help economists and regulators spot anomalies and resolve issues. Central banks will, however, need to make a concerted effort to overcome these risks and take steps to safeguard their investment.
Next Steps for Central Banks to Prioritize in the AI Journey
A clear AI strategy is key to resolving the obstacles that central banks and the wider financial sector face.
Several critical steps must be taken to ensure a successful strategy that fully harnesses the potential of next-level technologies. These include:
- Investing in advanced AI infrastructure, including hardware, software, and data management systems.
- Putting in place robust data governance structures and risk management practices to mitigate the potential risks associated with AI, such as model risk, data quality issues, and operational failures.
- Hiring skilled professionals and developing internal expertise through training and upskilling or reskilling programs.
- Creating and deploying ethical and regulatory frameworks and guidelines for the use of AI, addressing issues such as bias, transparency, and accountability.
- Implementing robust cybersecurity measures to protect AI systems and data from potential threats and breaches.
- Enhancing supervision and monitoring of AI applications in financial institutions to ensure compliance and risk mitigation.
- Collaborating and partnering with other central banks, financial institutions, and research organizations to share knowledge, best practices, and technological advancements.
- Providing clear communication to the public about the use of AI in the financial sector and how it impacts financial stability, consumer protection, and privacy.
Data availability and data governance are the cornerstones of a truly effective AI strategy. Once that foundation is in place, the rest can follow. Getting it right requires cooperation at an industry level.
The Need for Collaboration Among Central Banks
A recent Bank for International Settlements (BIS) report on the implications of AI called on central banks to “come together and foster a ‘community of practice’ to share knowledge, data, best practices, and AI tools” – because this will drive more rapid and widespread sector progress.
An open exchange of knowledge can help all institutions improve their AI strategies, accelerate the development of best practices in innovation and research, and enhance the overall quality of decision-making.
Collaborative efforts will also help drive the development of common, consistent standards and frameworks for the ethical use of AI, data privacy, and cybersecurity. The exchange of information about cyber threats and vulnerabilities will also equip central banks to plan and roll out robust defense strategies, as well as identify and mitigate cross-border risks, and support more effective crisis management.
An example of such a framework is the European Union’s AI Act, which was enacted and came into effect on 1 August 2024. It’s the first major law around the technology, aiming to regulate how organizations develop, use, and apply AI. The law is intended to protect against many of the risks AI poses while simultaneously fostering innovation. It is very much in its infancy, however; significant collaboration between different industries and enterprises, including central banks, will be required to ensure its efficacy.
Working hand in hand not only ensures consistency in how AI technologies are implemented and regulated across different jurisdictions but also better protects the global financial system.
Unlocking the Future Benefits of AI for Central Banks
Fostering greater industry-wide collaboration is going to be critical for central banks to effectively implement monetary policies. At the same time, they must take the necessary steps to prepare for the rapid technological advancements in AI to manage financial stability.
Central banks will need to invest in a culture of continuous learning and adaptation to keep up with advancements; build agile, flexible, and scalable IT infrastructure that can integrate new capabilities rapidly and effectively; establish innovation labs and research centers to experiment with emerging technologies; develop strategic plans and robust frameworks for evolving regulations; and collaborate with other industry players to remain resilient and competitive.
Those who take these steps will be able to overcome the risks the technology poses and enable business and wider macroeconomic benefits.
Invest in the Next Step of Your AI Journey
Banwari Agarwal is the CEO of Banking, Insurance, Retail, Manufacturing, Travel, and Logistics at Sutherland. Banwari brings deep expertise in digital technologies and operations and over 25 years of leadership experience across the US, Europe, and APAC. His strategic vision has driven transformative outcomes in digital business services across multiple industries, delivering innovative, cutting-edge solutions in finance, HR, procurement, and supply chain management.
