Good morning. It’s good to be here at DC Fintech Week. Thank you, Chris [Brummer], and all the organizers for inviting me here today. As is customary, I’d like to note that my remarks are my own, and I’m not speaking on behalf of the Commission or SEC staff.

Chris, before we get to your questions, I wanted to set the stage a bit about how I think about financial technology, or fintech.

Finance and technology have coexisted in a symbiotic relationship since antiquity. Think about it: Money, accounting, and ledgers, which we take for granted in some way now, were technologies that changed the face of finance long ago. Other technologies over the centuries that we also take for granted didn’t exist in earlier times — things like present value formulas, joint stock companies, derivatives, telephones, and securitizations.

While some may not consider those to be financial technologies, I think of fintech in the context of how the Financial Stability Board defined it: as technologies that may have a material effect on how finance is delivered.[1]

Putting it in this broader context is important. It’s not only startups disrupting the traditional business models of finance; it’s also financial incumbents themselves and big tech firms. All three are competing and to varying degrees incorporating new technologies into their business models and product offerings.

Every five or 10 years, major new financial technologies come along to change business models. One thing that I’ve come to believe is that technology can bring greater access, efficiency, and innovation in our capital markets, as well as economic growth.

Our central question is this, though: When new technologies come along and change the face of finance, how do we continue to achieve our core public policy goals?

The internet, mobile phones, and the cloud are already largely embedded in the financial technology stacks. What in the 2020s is challenging current business models in finance?

There are many, but I’d like to focus on two that are not yet as embedded in the technology stack. I’m sure you all want me to focus on crypto, but I’ll start with artificial intelligence and predictive data analytics.

Currently, I believe machine learning and artificial intelligence are changing decision-making and the models behind that decision-making more dramatically than crypto.

Predictive data analytics, including machine learning, are increasingly being adopted in finance — from trading, to asset management, to risk management. Though we’re still in the early stages of these developments, I think the transformation we’re living through now could be every bit as big as the internet was in the 1990s.

These new tools are transforming user experiences, allowing companies to manage risks, and increasing financial inclusion. Heck, it may well be that machines are listening to and analyzing this speech right now, through a process called sentiment analysis.

While these developments can increase access and choice, they also raise important public policy considerations, including conflicts of interest, bias, and systemic risks.

First, the application of digital analytics raises new questions about conflicts. Are these platforms solely optimizing for our returns as investors? Or are they also optimizing for other factors, including their revenues?

Second, how can we ensure that new developments in analytics don’t instead reinforce societal inequities that may be embedded in data?

Further, how does one explain increasingly complex, non-linear, and hyper-dimensional correlations that we’re seeing in modern algorithms?

These debates are not new. The Equal Credit Opportunity Act, passed 47 years ago this month, ensures for fair and unbiased access to credit. Technologies that came along in the 1950s and 1960s — namely, credit cards and consumer credit scores — led to the passage of the Fair Credit Reporting Act, which was passed 51 years ago next week. If someone is denied a loan, this law says, they deserve to know why. Similarly, I think in the 2020s, in the face of predictive data analytics, we ought to consider our policies to guard against bias and facilitate explainability.

Third, how do we guard against herding, interconnectedness, and concentration into certain datasets, providers, or investments? This can lead to system-wide issues.

Another area reshaping business models is crypto. Satoshi Nakamoto’s innovation 13 years ago has been a catalyst for change. It challenged some early technologies — money and ledgers — as well as incumbent business models and the official sector.

Nakamoto’s work and the subsequent emergence of crypto assets has spurred a number of other innovations too. For example, the public for centuries has accessed stock exchanges through a broker — a member of the exchange. By contrast, crypto trading and lending platforms allow millions of individuals (who choose to do so) to gain direct access via open application programming interfaces (APIs).

Another innovation within the crypto space is so-called decentralized finance (DeFi). While primarily still within the crypto asset space, DeFi is challenging current business models in trading, lending, and other aspects of finance.

Yet another innovation is stablecoins, currently valued at about $130 billion, which are intertwined within these crypto trading, lending and DeFi platforms.[2] Stablecoins facilitate the bulk of crypto trading and lending.

I am technology-neutral, but I am anything but public policy-neutral. As new technologies come along and change business models in finance, we need to continue to work toward our public-policy goals. That was true of the television, the internet, and other new forms of communication; it was true of derivatives and swaps; it’s true of today’s developments and the ones that will come in the 2030s and 2040s that we cannot picture today.

How do we continue to protect investors? How do we maintain fair, orderly, and efficient markets, and facilitate capital formation? How do we guard against illicit activity? How do we ensure for financial inclusion and access, and guard against bias? How do we promote financial stability and robustness?

The technologies may be new, but these public policy questions are timeless.

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