(Source- scmp)
Central banks globally face an urgent need to expand their generative artificial intelligence (AI) capabilities to navigate emerging risks and opportunities brought about by advanced technologies that mimic human cognition. This is the core finding of a recent study by the Bank for International Settlements (BIS), which underscores the transformative impact of AI on global finance.
The Enormous Potential of AI in Finance
A new study by the Bank for International Settlements (BIS) emphasizes the profound implications of AI on global finance, highlighting both its potential and risks. The McKinsey Global Institute estimates that AI could add up to $340 billion annually to the global banking sector, representing 4.7% of total industry revenues. This gain is primarily attributed to increased productivity, with 70% of financial services firms already employing AI to improve cash-flow predictions and liquidity management.
Futurist Ray Kurzweil predicts that AI will surpass human capabilities in all cognitive tasks within the next few years. According to the BIS, these advancements can be harnessed by central banks to achieve their policy objectives, revolutionizing their operations. However, this path is fraught with challenges, including the risk of inflation.
AI offers central banks the tools to foresee risks more accurately and identify factors that could lead to a recession. These technologies can enhance fraud detection and streamline bank operations, such as managing the money supply more efficiently. The adoption of central bank digital currencies and advancements in blockchain technology also stand to benefit significantly from AI.
Navigating Ethical, Legal, and Technical Challenges
While AI presents substantial opportunities, it also poses significant ethical, legal, and technical challenges. Central banks are particularly vulnerable to cybersecurity threats and data biases that could skew decision-making. The rapid pace of technological change adds to the complexity, making it difficult for central banks to adapt without misallocating resources. Central banks must clearly define how AI can support their roles in maintaining financial stability, preventing fraud, and serving as lenders of last resort. However, the traditionally risk-averse nature of central banks complicates this adaptation process.
Scaling AI capabilities requires skilled human resources, a commodity in short supply globally. The private sector often offers more competitive compensation, making it challenging for central banks to attract and retain talent. In-house training programs are essential, but central banks must also explore partnerships with the private sector to share expertise without compromising their independence. Collaborating on cloud computing initiatives could help central banks distribute investment costs and keep pace with rapid technological advancements.
Addressing AI’s Inherent Risks
The nature of AI itself introduces new risks, including threats to data privacy and potential biases. The commodification of data could lead to privacy violations, with individuals’ personal information being used without consent. Different countries are grappling with their own regulations, but there is no global consensus on data protection standards. Embedded biases in AI, stemming from incomplete historical data or human errors, could result in discriminatory and unfair decisions, exacerbating societal inequalities. The BIS warns that such biases could deny certain groups access to credit, deepening disparities.
AI also introduces new cybersecurity vulnerabilities, such as prompt injection attacks and data poisoning, which can manipulate AI systems to leak sensitive data or spread misinformation. These threats highlight the need for international organizations, including the International Monetary Fund and the BIS, to adapt their roles in response to AI. The future of Bretton Woods institutions should include a focus on addressing the challenges and opportunities presented by AI.
In conclusion, as central banks strive to integrate AI into their operations, they must navigate a complex landscape of opportunities and risks. Effective collaboration, robust ethical standards, and adaptive regulatory frameworks will be crucial in leveraging AI’s potential while safeguarding financial stability and societal equity.
