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Machine learning is changing the way we invest. Is that a good thing?

As the internet has become increasingly embedded in our lives, we’ve started to see a lot of new features across consumer technologies. Whether we’re working out, watching a movie on a streaming app, we see things like points, leaderboards, and prompts to increase our engagement.

Underpinning many of these features is predictive data analytics. This allows companies to analyze the success of individual prompts and to increase activity.

These types of features are implemented across many different technologies. For example, the streaming apps figured out I’m kind of a rom-com guy. I’m more likely to spend more time in an app if they suggest a rom-com than if they recommend some other movie.

If we watch a movie that a streaming app recommends and don’t like it, we might lose a couple of hours of an evening. Following the wrong prompt on a trading app, though, could have a substantial effect on a saver’s financial position.

Many of these features encourage investors to trade more.

In the last few years, we’ve seen new brokerage apps, wealth management apps that give us investing advice, along with robo-advising. Underneath these new user interfaces and technologies is also a vast change in data analytics called machine learning.

While these new technologies can bring us greater access and product choice, they also raise questions as to whether we as investors are appropriately protected when we trade and get financial advice.

To that end, I’d love to assign just a wee bit of “homework” in this week’s Office Hours.

We’ve just put out a request for comment on digital engagement practices, such as gamification. And we’d love to hear from you about how you use these tools and what think and like to see from the SEC.

Please head to sec.gov to submit your comments. Thanks!