Thank you, Secretary Yellen. I thank the staff for their collaboration on the Financial Stability Oversight Council annual report, which I am pleased to support.

When we gathered last December, we spoke about uncertain times in the global economy and financial markets—from the war in Ukraine to central banks tightening their policy stances.

One year later, unfortunately, the world is experiencing yet another major war. Further, with rising interest rates, we experienced regional bank failures, leading to the extraordinary intervention by the agencies around this table to prevent contagion spreading to the broader economy. In addition, as discussed by this Council as well as in the annual report, there currently is significant leverage facilitated by the relationship between hedge funds on the one hand and banks and broker-dealers on the other.

These events once again highlight the importance of our shared work to promote financial stability.

At the SEC, working with many of you, this year we finalized multiple rules related to resiliency. We finalized rules to enhance money market funds’ resiliency, shorten the settlement cycle to one day, and to require current reporting on Form PF by private fund advisers of certain material events.[1] Yesterday, we finalized rules regarding central clearing in the $26 trillion Treasury markets.[2]

Working with many of you, we also have important ongoing work on resiliency. We continue work regarding proposals on open-end fund liquidity management, including work with bank regulators on potential similar rules for collective investment funds.[3] We are working alongside the Commodity Futures Trading Commission on updating periodic filings on Form PF by private fund advisers.[4] We’re continuing to consider comments on proposed rules regarding cybersecurity for broker-dealers, trading platforms, and investment advisers.[5]

I’d like to say a few words, though, on artificial intelligence and financial stability.[6]

First, I believe artificial intelligence is the most transformative technology of our time, on par with the internet and mass production of automobiles.

AI’s predictive data analytics and pattern recognition, particularly when done at scale, can create great efficiencies across the economy.

In finance, there are potential benefits of greater financial inclusion and enhanced user experience. AI already is being used for call centers, account openings, compliance programs, claims processing, trading algorithms, sentiment analysis, and more. It’s also fueled a rapid change among robo-advisers and brokerage apps.

AI also has raised challenges.

At the micro level, as the FSOC annual report details, there are challenges regarding explainability, bias, and accuracy. AI also can be used by bad actors to deceive people in the markets.

At the macro level, there is something that goes beyond that discussion.

The possibility of one or even a small number of AI platforms or data aggregators dominating raises issues with regard to financial stability.[7]

Given that AI relies on an insatiable demand for data and computational power, there can be economies of scale and data network effects at play. We’ve already seen companies, both incumbents and startups, relying on base or foundation AI models and building applications on top of them.[8]

AI may heighten financial fragility, as it could promote herding among individual actors making similar decisions as they get the same signal from a base model or data aggregator. This could encourage monocultures. It also could exacerbate the inherent network interconnectedness of the global financial system.

Thus, AI may play a central role in the after-action reports of a future financial crisis.

While current model risk management guidance—generally written prior to this new wave of data analytics—will need to be updated, it will not be sufficient. Model risk management tools, while lowering overall risk, primarily address firm-level, or so-called micro-prudential, risks.

Further, the agencies around this table generally regulate particular entities or activities.

Many of the macro challenges to financial stability that AI may pose in the future, though, will require new thinking on system-wide or macro-prudential policy interventions.

In particular, I think we all will need to think about the dependencies and interconnectedness that these entities or activities across the financial system may have to an AI model or data aggregator.

Thank you.