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Prepared Remarks Before the Asset Management Advisory Committee

Washington D.C.

Sept. 27, 2021

Thank you, Ed [Bernard]. I’m glad to be with the Asset Management Advisory Committee again. I appreciate the members’ time and willingness to give us advice, and I look forward to hearing the readouts from today’s discussions. As is customary, I’d like to note I’m not speaking on behalf of the Commission or the SEC staff.

Today, I’d like to speak about a topic that I know your Evolution of Advice Subcommittee regularly takes up: the way rapidly changing technology is changing user experiences and marketing, providing the ability to give individuals personalized advice and client service.

I’d like to discuss something underlying all of this: predictive data analytics.

We are living in a transformational time, perhaps as transformational as the internet itself. Artificial intelligence, predictive data analytics, and machine learning are shaping and will continue to reshape many parts of our economy.

To take just one example, I believe we’re in an early stage of a transition toward driverless cars. Policymakers already are thinking through how to keep passengers and pedestrians safe, if and when these changes take hold.

Finance is not immune to these developments. Here, too, policymakers must consider what rules of the road we need for modern capital markets and for the use of predictive data analytics.

You see, new platforms can collect boundless amounts of data — from customers or from the world around them. With that data — say, the steps we’ve taken wearing our fitness bands, or the days of the week we buy pet food online — they can tune their marketing to each of us differently.

Therefore, fintech platforms have new capabilities to tailor marketing and products to individual investors, using predictive data analytics and other digital engagement practices (DEPs).

These technologies can bring increased efficiencies and greater access in finance. In many cases too, though, these individualized features may encourage investors to invest in different products or change their investment strategy.

Thus, in the case of robo-advisers or investment advisers, I question what are they doing within the predictive data analytics algorithms — if, statistically speaking, they are maximizing for our returns as investors, or, say, the revenues of the platforms.

In essence, predictive data analytics and other DEPs, including behavioral prompts and differential marketing, often are designed, in part, to increase platform revenues, data collection, and customer engagement.

This raises some key questions:

How are investors protected in light of the potential conflicts of interest that may exist when DEPs optimize for revenues, data collection, or investor behaviors?

There’s a related policy question: if DEPs are affecting investors’ behavior, when is that a recommendation or investment advice?

How do these new business models ensure for fairness of access and pricing? More specifically, this question arises when the underlying data used in the analytic models reflects society’s data, with historical biases that may be proxies for protected characteristics, like race and gender.[1]

Advances in predictive data analytics also could raise some system-wide issues when we apply new models and artificial intelligence across our capital markets. This could lead to greater concentration of data sources, herding, and interconnectedness, and potentially increase systemic risk.

We’re taking a look at these issues as part of a broader examination of predictive data analytics and the intersection between finance and technology.

In late August, the Commission published a request for public comment on the use of new and emerging technologies by financial industry firms.[2] I encourage investors in your funds to weigh in by Oct. 1.

Separately, I have asked SEC staff to develop proposals for the Commission’s consideration on cybersecurity risk governance — both on the issuers’ side and on the funds’ side. These could address issues such as cyber hygiene and incident reporting.

I look forward to your thoughts on all these topics.

Thank you.


[1] See Gary Gensler and Lily Bailey, “Deep Learning and Financial Stability,” available at

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