Why Clients Leave
Ram Nagappan, chief information officer at Pershing, says machine learning can show patterns with clients and help predict when they might defect for another firm.
“We are using machine learning to give some kind of head’s up that this client could potentially leave,” Nagappan says. “We take data on who is the client, what gender, what status, tendency, tenure, all types of information that you have on the book of the business, then we use the previous history of why a client left and recreate a pattern. … This is a learning technique. You want to learn whether it is right or wrong and then you can correct the algorithm.”
Nagappan says the machines look at the assets a client has, the assets she’s transferring over a period of time, what similar clients did during book transfers and what transactions they pursued—as well as the clients’ ages, genders and employment statuses, dependency and how much online interaction they have had. The algorithm then shows patterns in a book of business similar to the patterns of clients who left in the past, Nagappan says. If someone is doing money transfers, or logging in and checking more frequently than ever, those are warning signs.
He mentions the big improvements in AI tools by Microsoft, Google and Amazon. Pershing offers these through its own portal and offers tools through its advisors’ own portals.
Not A Replacement
But still, even in world of digital advice, clients still want to engage with human beings, says McMillan. Morgan Stanley, for one, has no intention of replacing them.
“We fundamentally don’t think that’s the game we want to be in, nor should we be in,” McMillan says. AI and machine learning technology does things that are unique, he says, including storage and memory, things humans aren’t equipped for. “They are able to process millions and billions of permutations. They don’t forget things, they look for anomalies.” But humans are able to infer things from other people, read gestures and faces. “And they are able to contextualize multiple data facts and assimilate them in ways that [robots] simply can’t do yet.”
Hype aside, there is still a powerful gee-whiz quality to AI that allows the visionaries to come out, says Meghji, and there is lots of room for disruptors. He sees a big one coming in the insurance industry, which he says is a field about to be invaded by robots. Robo-platforms here will likely do the same things they do for investment platforms—finding not ETFs but insurance policies for people who need to be covered and have only to pop in their risk factors on their home computers.