The AI Vanguard:
Embracing Innovation in Central Banking
With the relentless march of progress and the rapid advancements in technology, artificial intelligence has made historic strides. AI has infiltrated our homes in various forms, from ChatGPT to Wen Xin Yi Yan, and its formidable computational prowess is widely applied within the financial sector’s market and trading operations, gaining favor with numerous central banks. The Federal Reserve is now researching how to incorporate AI into its operations. This year, the Bank of England stated that it’s leveraging artificial intelligence to bolster its capabilities, including forecasting economic growth, banking distress, and financial crises. The European Central Bank has also begun to accelerate mundane tasks such as drafting briefs, compiling banking data, writing software code, and translating documents with the aid of AI. In fact, AI is not just revolutionizing the global economic and financial landscape but is also bringing about significant impact to the operations of central banks worldwide.
The Regulators’ Dividend:
Central Banks Reaping AI Development Rewards
As regulators in the economic and financial realms, global central banks are the undoubted beneficiaries of AI’s developmental “dividend.” On one hand, AI will influence central banks’ core activities in economic management. Typically tasked with fostering price and financial stability, central banks will find AI impacting the financial system as well as productivity, consumption, investment, and the labor market—factors which inherently affect price and financial stability directly. AI’s widespread adoption could enhance businesses’ abilities to quickly adjust prices in response to macroeconomic shifts, thereby influencing inflation dynamics. Generative AI can drive cost-efficiency and heighten automation in financial tasks, fostering a data-driven transformation in the financial sector and pushing the boundaries of AI applications within the industry. On the other hand, the deployment of AI will have a direct impact on central banking regulation, as financial institutions like commercial banks increasingly turn to AI tools—altering their interactions with and regulation by central banks.
Mission Enhancement:
Central Banks Leveraging AI For Their Charter
Central banks and other regulatory bodies may increasingly utilize AI to fulfill their mandates in areas such as monetary policy, regulation, and financial stability. For instance, AI’s prowess in analyzing vast amounts of real-time data can aid central banks in devising “real-time forecasting” systems for financial risk accumulation or predicting economic downturns.
Anti-money Laundering Advances:
The AI-Powered Fight Against Financial Crimes
The efficacy of AI in tracking money laundering activities is notable. Anti-money laundering projects by several country’s central banks have tested AI’s ability to detect “dark money” in payment data, finding that machine learning models outperform traditional methods. Furthermore, AI can directly enhance cognitive tasks, making regulation by central banks more efficient.
The Coin’s Other Side:
AI Risks for Central Banks
However, “every coin has two sides,” and due to its inherent risks, AI could negatively impact central banks. For instance, AI models could be susceptible to “data poisoning attacks,” making them vulnerable to manipulation by unknown entities. Moreover, the widespread use of AI might lead to biases and discrimination, provoke data privacy issues, and create dependency on a few AI model providers. If numerous financial institutions employ identical algorithms, financial stability could be at risk. This might exacerbate herd behaviors and liquidity hoarding, runs on banks, and fire sales, amplifying procyclicality and market volatility.
Overall Assessment:
AI’s Broad Application—A Double-edged Sword for Central Banks
The rapid and extensive application of AI presents both benefits and challenges to global central banks. In facing these new challenges, whether as informed observers of technological impacts or as users of the technology themselves, central banks need to enhance their capabilities. As observers, central banks must monitor the shock AI imparts on aggregate supply and demand, staying ahead of how AI impacts economic activity. As users, central banks need to accumulate expertise in integrating AI and non-traditional data into their analytical tools. In employing external and internal AI models as well as collecting and sourcing internal data versus purchasing data from external suppliers, central banks must make more prudent trade-offs. Data availability and data governance are key enablers for central banks’ use of AI, both of which rely on international cooperation. Hence, central banks across the globe need to reinforce collaboration and establish a “community of practice” for sharing knowledge, data, and best practices.
