AXA I.M. wants A.I. to help it see clients’ big picture
Instead of sales people getting caught up in the gears of short-term activity, A.I. can help them understand clients better.
AXA Investment Managers, with €735 billion ($854 billion) of assets under management, is using machine learning to improve client onboarding and reporting. The ultimate goal is to enhance fund distribution and improve the digital client experience, says Jean-Pierre Leoni, AXA I.M.'s global head of client management in Paris.
“Client experience is important because it’s what the client sees,” he said.
This includes the sales process, client onboarding, and reporting.
Pitches remain based on personal relationships, but Leoni wants to see salespeople become more adept at using data to understand their customers better.
Artificial intelligence tools can organize customer information, such as client-meeting notes, in a better way, as machines scan notes, identify keywords, and flag patterns or changes. In theory, this should arm salespeople with better insight, particularly for longstanding customers.
A different kind of machine
“The sales process today is like Charlie Chaplin in ‘Modern Times’,” Leoni said. With Chaplin stuck between gigantic gears of commerce, “He’s always in a rush, without having any idea of the big picture.”
The most important part of a client meeting is to understand what institutions want. “But to know what they want can also be very hard,” Leoni said. Clients might be too polite to spell out their demands, while salespeople may not listen or get overzealous.
It can take months to close a mandate, or years to win big institutional tickets. The firm wants to use digital technology to make that time spent more efficient and insightful, by sharing information internally among all stakeholders.
“Digital won’t solve this, but it can help,” Leoni said, adding that the machine-learning tools are still being tested at AXA I.M.
AXA I.M. is involved in a broader move to digitize the client experience by 2020, which involves not just the tech itself, but embedding its use culturally throughout the organization.
Investing in digital solutions like machine learning is different than traditional I.T. budgeting, Leoni says, because the outcomes are less certain.
But, speaking to DigFin, Leoni put a finger in the air, as though to gauge which way the wind is blowing. “You need to take some risk,” he said.