What if artificial intelligence could tell you that your client was exhibiting some of the behavioral patterns of other advisory clients who have fired their advisors? Actually, this capability already exists, and it’s one of the ways that AI will change the RIA business.

Many, if not most or all of the platforms serving financial advisors offer A.I.-generated tools. BNY Mellon | Pershing’s platform, for example, offers A.I.-assisted account transfers designed to make that process much easier. Another feature on that platform involves trade surveillance where A.I. alerts an advisor about a trade they’re trying to make that might not pass the sniff test with compliance. One particularly intriguing A.I. tool sends signals to an advisor that a client is not happy with the relationship.

As to how A.I.-generated data interacts with advisors, Ram Nagappan, chief information officer at BNY Mellon | Pershing, says when an advisor looks at a client’s name on their dashboard they’ll see various color-coded signals on the side representing a prioritized list of information, or indications about the client derived from the A.I.

As for those signals pertaining to the client relationship, an assessment is delivered to the dashboard in the form of a relationship health score that translates risk (i.e., client unhappiness) into a range ranked from highest to lowest. Some of the factors used to assess relationship risk include recent account transfer requests to other financial institutions; the performance of the client’s accounts relative to the overall market; and poor responses to client surveys and feedback, as well as chat sentiments.

Other factors include how frequently an advisor met with the client during the past year, and whether a financial plan exists and, if so, when it was last updated.

In addition to these factors, Nagappan explains, A.I. can analyze historical examples of clients who already left their advisors and can look for additional signals that may be relevant to assess client relationship health.

And so artificial intelligence, which seems so futuristic, is very much in the here and now in the advisory profession. But what exactly is it?

Demystifying A.I.
During a two-hour broadcast on PBS’s investigative documentary series Frontline that examined the perils and promise of artificial intelligence, the show’s longtime narrator, Will Lyman, whose sonorous voice would likely lend an aura of drama and gravitas even to his reading of a dinner menu, had this to say about the subject: “The A.I. algorithms are ushering in a new age of great potential and prosperity, but an age that will deepen inequality, challenge democracy and divide the world into two A.I. superpowers.” Those being the U.S. and China.

That’s ominous stuff. As A.I. has continued to amaze—if not alarm—the world since the Frontline episode aired in 2019, the technology is increasingly gaining traction in the financial services world. And that includes the wealth management space, as the Pershing examples show. That said, many people within the industry still don’t know what to make of A.I., or even really know what it is.

“When I meet with CEOs of [wealth management] organizations, I ask if they can tell me what A.I. means, and they shrug their shoulders and go, ‘I don’t know, but I read about it all the time and I think I have to have it,’” says Brandon Rembe, chief product officer at Envestnet, whose wealth management platform offers advisors a variety of A.I.-driven tools.

“It used to be ‘big data’ was the buzzword,” he says, “but it has shifted toward A.I. A lot of people conflate A.I. with business intelligence reporting, but they’re very different. I think the industry is trying to figure out what A.I. is; it’s a hard definition to pin down.”

A.I. is a broad term that’s classified into branches, subsets or types. And the number of these different categories can vary, depending on the source. According to IBM, any system capable of simulating human intelligence and thought processes is said to have artificial intelligence.

Machine learning, which is a branch of A.I., employs algorithms enabling computers to parse data, learn things and perform human-like thinking—presumably at a level that exceeds human capability. For example, the IBM supercomputer named Watson used machine learning to defeat the two all-time champions of the game show Jeopardy in a two-round match in 2011.

Other A.I. branches include deep learning (a more advanced form of machine learning using neural networks applied to larger data sets), natural language processing (which helps machines understand human language), fuzzy logic (a reasoning method that converts subjective, or “fuzzy” input into concrete results) and robotics, among others.

According to people in the know, A.I. requires a feedstock comprising reams and reams of data—coupled with smart individuals to turn that data into algorithms—to create actionable insights and real-world solutions.

“At Envestnet we have millions of investor accounts and analyze billions of transactions every month and have multiple trillions in assets that we look at,” Rembe says. “Those are the types of numbers you need to look at to scale A.I. and have it be meaningful.”

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