Central banks have been seeking assistance from former central bankers to improve their forecasting abilities. The Riksbank enlisted Mervyn King, former governor of the Bank of England, to evaluate their forecasting from the 2010s, while the Bank of England recently turned to Ben Bernanke, former Fed chair, to review their methods. Bernanke’s assessment pointed out that central banks’ forecasting has been subpar due to global shocks and outdated systems at the Bank of England.
The utilization of artificial intelligence (AI) in central banking is on the rise, with some central banks in emerging economies like Indonesia employing AI to monitor public reactions to monetary policy. The European Central Bank (ECB) has initiated the Athena project, which utilizes AI to assist banking supervisors in detecting anomalies in documents. Additionally, central banks are keeping a close watch on fintech companies that use AI for credit allocation and investment strategies, posing challenges for regulators in understanding the AI models behind these services.
As the EU AI Act and quantum computing in finance continue to advance, central banks must enhance their use of AI tools. The consensus is that AI-driven outcomes should support, not replace, economists and supervisors. However, obstacles such as outdated IT systems, limited data management capabilities, and a shortage of AI expertise within central banks remain. Data is recognized as a major challenge, with central banks potentially benefiting from access to high-quality datasets that could offer better insights for monetary policy and fraud detection.
The next generation of central bankers may leverage their supervisory authority to acquire high-frequency datasets from payment providers, startups, and financial institutions to inform monetary policy decisions and detect fraud. The connection between central bank digital currencies and AI is seldom discussed, yet the implementation of digital currencies could provide valuable data on household financial behavior for policy adjustments. Central bankers will need to adjust their mindsets and communication methods to effectively harness AI in their decision-making processes.
In conclusion, central banks are increasingly turning to AI for forecasting and supervisory purposes. Despite challenges such as outdated systems and data management issues, central banks have the opportunity to utilize AI for more precise and well-informed monetary policy decisions. The potential advantages of incorporating AI, alongside advancements in data collection and central bank digital currencies, have the potential to transform the operations of central banks in the future. Adapting to these changes will require a shift in mindset and communication strategies for central bankers.
