Artificial intelligence (AI) is seen as becoming an increasingly important tool for financial advisors, but thought leaders in the field urge firms to use caution despite the lure of potential rewards.
It is likely that AI – as it continues to be developed – can help financial advisors better understand a client’s risk profile and spending habits.
“Advice can be provided faster [with AI],” says Reena Aggarwal, a professor at Georgetown University, where she directs the Center for Financial Markets and Policy.
“AI [also] can help clients make better decisions.”
Yet, there are concerns. On one level, investment advisors need to be careful about the quality of the data and algorithms being put into use.
“AI can only work when you have good data,” Aggarwal warned. So, the data must be timely, accurate and correct, she said. Some AI can be predictive and that means using assumptions – which could be inaccurate, she adds.
“In terms of the actual advice that financial advisors provide, the use of AI could certainly help, but the advisors should be more cautious and stay aware of the limitations of AI,” Jillian Grennan, a professor at Duke University, agreed.
Based on her recent research, Grennan explained that data scientists are making the “implicit assumption” that the data-generated process itself is not changing over time. “But … the underlying data-generating process is being affected by these fintechs,” she suggests. She found that “sell-side equity analysts produce more biased, less accurate reports where fintechs concentrate.”
“I think the challenge for financial advisors will be to figure out if the AI they are using may in fact change the data-generating process,” she said.
So, firms need to know if fintechs that provide signals, or use AI to provide signals, are updating their algorithm continuously, she said.
Vasant Dhar, a professor at New York University, even questions if advanced offerings are yet available – though he says they will become more prevalent – where products are tailored through AI and are matched with investors.
“The systems really don’t exist,” Dhar said. “If it does, I would be highly skeptical.”
Dhar, who developed the Adaptive Quant Trading (AQT) program for SCT Capital Management, a hedge fund based on machine learning, said that his offering is different from a type of offering where products are individualized to a client’s risk profile.