Machine learning (ML) and artificial intelligence (AI) will meaningfully improve the way the Federal Reserve (Fed) designs interest rate policy and manages financial stability risks in the United States. These two key responsibilities are entirely dependent on the quality of the Fed’s data, modeling, and surveillance of the economy and capital markets. With ever increasing amounts of data produced by firms and the economy at large, ML will significantly expand the Fed’s ability to understand and forecast market conditions .
Setting Interest Rates and Financial Stability
Machine learning can solve two main difficulties with business cycle analysis. First, the Federal Open Markets Committee (FOMC) relies heavily on lagging economic indicators to make its interest rate decisions. For instance, in the US, the unemployment rate is surveyed and computed only once a month . During a financial crisis, relying on the monthly unemployment rate will leave the Fed a step behind. Deep learning, however, can provide more accurate and faster “nowcasts” of key economic indicators given vast quantities of consumer and financial data available today .
Second, the Fed’s models are completely reliant on economic theory to understand the economy, requiring theoretical cause-and-effect links. As evident in the financial crisis, prevailing macroeconomic models did not appropriately model the effect of the financial sector for prediction purposes . Neural networks can uncover patterns in economic data “without the constraints of theory,” giving policymakers even more insight into where the economy is trending. 
Furthermore, ML can improve the Fed’s oversight over the stability of the financial system, a responsibility known as macroprudential supervision . First, ML can provide better “early warning” signs of potential bank failure by identifying crucial correlations between credit or deposit data and bank weakness . Second, ML can hep Fed regulators understand if banks are gaming capital regulations, given substantial grey area in bank capital standards and vast amounts of data used in capital stress testing. 
Machine Learning at the Fed
The Fed itself is approaching ML through a decentralized approach, allowing the 12 regional Fed banks to independently pursue disparate strands of ML research. Today, the Fed is already using automated ML “heat maps” in its annual bank capital assessments (CCAR) to uncover financial stability risks  as well as for back-testing and validating banks’ capital loss models . The Fed also uses natural language processing tools at large financial institutions to examine emails and search for potential signals of control failures or misbehavior .
Researchers at the Federal Reserve Bank of Kansas City have developed neural network models which can more accurately forecast unemployment than any other currently existing models . And today, three regional Federal Reserve banks publish their own online ML-based nowcasts of GDP or inflation. Although the FOMC does not yet refer to nowcasts in its interest rate announcements, it seems likely that in the medium term, these ML driven forecasts will become an important datapoint for the Fed’s monetary policy, especially as more data is collected.
However, there is no unified ML or AI strategy at the Fed currently. I would recommend that over the short term, the Fed: 1) Pursue a more centralized approach to developing its ML strategy. The potential benefits of ML in accuracy and speed necessitate a more coherent approach. The Board of Governors should convene a task force to identify the ways the Fed could best implement ML in its monetary policymaking, instead of relying on bottoms-up innovation from regional Fed banks. 2) Collaborate with other central banks to develop ML policymaking best practices. Global central banks are all facing similar ML issues, and financial stability and monetary policy is inseparably linked across markets. The Fed should work with other leading central banks to harmonize approaches to ML and share best practices.
In the medium-term, the Fed will need to develop the necessary private consumer and financial data streams necessary to create meaningful ML models. ML requires copious amounts of data, and the Fed may not currently have access to crucial relevant datasets, including credit card, web search, and financial market data. The Fed should work with Congress, other regulators, and the private sector to find privacy-protected ways to obtain these data to power its ML models.
Finally, there still remain crucial questions for the Fed’s use of ML in its fundamental responsibilities. For interest rate setting, given the limited historical span of market data and the even smaller number of recessions, to what extent can the Fed produce meaningfully predictive economic models? In its role as a regulator, are there other ways that the Fed can deploy ML to provide stronger macroprudential supervision of financial markets?
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