Artificial intelligence is giving finance a boost — through robo advising, its ability to improve fraud detection and claims processing, and more.
Despite the upsides, there are risks and public policy challenges that must be considered, said Gary Gensler, chair of the Securities and Exchange Commission and a former professor at MIT Sloan.
“I think that we’re living in a truly transformational time,” said Gensler, who spoke at the recent AI Policy Forum summit at MIT. Artificial intelligence is “every bit as transformational as the internet,” especially when it comes to predictive data analytics, “but it comes with some risks.”
During the conversation, Gensler shared his thoughts on how artificial intelligence is changing finance. Here are four of his takeaways:
AI in finance is especially complex
Having solid predictive models is crucial in AI, whether it’s in social media or in driverless cars. The difference with finance is that “the robustness of the network itself” matters just as much as the model.
“Finance — and particularly the capital markets — are probably one of the globe’s most complex networks,” Gensler said, especially considering “all the variables and all the adversarial competition in our capital markets.”
If predictive data analytics is a “new tool” for capital markets, the question becomes how to bring that under the realm of public policy.
“We normally look to attach public policy to activities — how you drive your car on the highway, how you sell a security. And we attach it to entities — a bank, a stock exchange, a hospital,” he said. “Here arguably you have a computer science tool that comes along, [and] that tool is also becoming an activity.”
Unbiased algorithms are important
Gensler highlighted the importance of having neutral algorithms that don’t put a platform or a business' revenue or profit ahead of fiduciary duty, to make sure people don’t get steered toward higher margin products or trading options.
“In the brokerage space, we have the duty of loyalty, where you’re supposed to put your client’s interest before that of the platform,” he said. “My call to action, maybe to the academics and computer scientists, is to help people think through this — how you could have a neutral algorithm that’s not putting a platform or a business’ revenue or profits ahead of the investing public.”
The U.S. doesn’t need AI-specific regulations
Should there be specific rules that are tailored toward artificial intelligence in finance? Gensler said no.
When new tools have come along previously, “We generally don't write new laws or regulations,” he said. In finance, “We’ve come to some consensus through our legislative bodies, and we’ve adopted laws to protect the public” across investor protection and financial stability. These are “tried and true public policies” and less “about a new law or a new rule about artificial intelligence.”
Keep an eye on predictive analytics
Gensler believes that predictive analytics are revolutionizing the financial industry but are an “emerging risk” and must be watched closely. “There are tradeoffs that come with new technologies,” he said.
He cited a possible problem with foundation models, as an example. In AI, foundational models are trained on large amounts of data that can be adapted and used for a wide range of cases; but they can easily become a “concentrated risk” if people rely on them too much.
Predictive data analytics have “remarkable abilities to predict things,” he said, but “I do think that there’s a risk that the crisis of 2027 or the crisis of 2034 is going to be embedded somewhere in predictive data analytics.”