Artificial intelligence (AI) is reshaping the financial landscape, presenting both opportunities and risks that demand urgent scrutiny from global central banks. As AI technologies become integral to various financial processes—ranging from risk management and fraud detection to investment strategies and customer service—central banks are tasked with adapting their operations to maintain price and financial stability. This necessity has been underscored in the BIS Annual Report 2024, which highlights how AI enables firms to adjust prices swiftly in reaction to macroeconomic shifts.
Central banks are increasingly leveraging AI tools to enhance monetary policy, supervision, and overall financial stability. The rapid integration of AI into the financial system requires central banks to rethink their roles, evolving from traditional data compilers to proactive users and providers of analytical models that incorporate non-traditional data sets. “For better monetary policy, the application of AI is no longer a future-oriented idea; it is now a reality,” said a central bank official, emphasizing the urgency for adaptive strategies.
However, the rise of AI also poses significant challenges. While these technologies can streamline operations and increase efficiency, they may also compromise financial stability. For instance, algorithmic trading platforms can trigger rapid market disruptions, commonly known as flash crashes. Moreover, the potential for cyberattacks increases as criminal organizations exploit vulnerabilities in financial systems, potentially leading to synchronized attacks on critical infrastructure.
Central banks must navigate the “defender’s dilemma,” where attackers need to find only one weakness to exploit, while defenders must safeguard an entire system. As AI adoption accelerates, this dilemma could worsen, necessitating a cautious yet proactive approach from central banks to mitigate such risks.
The evolution of AI, from its inception in the late 1950s to advancements in machine learning and deep learning, reflects its growing complexity and capability. Machine learning allows for pattern recognition across vast datasets, while deep learning processes unstructured data in ways that mimic human cognition. This transformation compels central banks to reassess their operational frameworks in light of AI’s capabilities and implications.
AI’s influence extends to four crucial areas: payments, lending, insurance, and asset management. Its implementation in these sectors has been shown to improve efficiency and reduce operational costs, particularly in back-end processing and regulatory compliance. However, the technology also raises concerns about potential herd behavior among investors, the risk of misinterpretation of AI-driven decisions, and the possibility of exacerbating existing financial crises.
To effectively integrate AI into policy frameworks, central banks must prioritize data availability and governance. A key challenge remains the balance between utilizing in-house models versus relying on external solutions. While external models can be cost-effective short-term, they may lead to concentration risks, exposing central banks to a few dominant providers. Furthermore, challenges such as the need for robust data governance frameworks, IT infrastructure, and staff training are critical hurdles to overcome.
To address these challenges, sound data governance practices are essential. Developing nations, in particular, lag behind their developed counterparts in this regard. Central banks must foster communities of practice, facilitating knowledge sharing, best practices, and AI tools among themselves to navigate the evolving landscape effectively.
As central banks grapple with these complexities, the integration of AI in monetary policy is not just a matter of modernization; it is essential for fulfilling their dual mandate of maintaining economic stability and fostering growth. The urgency to adapt to AI technologies has never been clearer, and the effective implementation of these tools will be pivotal in shaping the future of the financial system.
Moazzem was a former deputy director at the central bank of Bangladesh.
Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the opinions and views of The Business Standard.
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