When you’re managing trillions of dollars in client assets, it’s important to get things right.
For Northern Trust, the almost 140-year-old Chicago-based bank with offices around the world, properly accounting for every cent is just the starting point.
“People look at us to hold billions of dollars and make sure it’s safe,” says chief operating officer Pete Cherecwich.
The bank’s business includes holding and managing the investments of clients such as large family offices, pension funds, asset managers and university endowments.
Earlier this year, Northern Trust worked with Microsoft Frontier Company to explore how AI could speed up one of its most complex processes: onboarding new institutional clients, which can take weeks.
They knew what they wanted: an AI agent that can convert years of transaction history and cash flows — from multiple investments, in multiple formats — onto Northern Trust’s in-house platform, quickly and accurately.
They knew what they didn’t want: errors.
Building a rule library
Microsoft Frontier Company works by embedding highly skilled engineers and other AI experts in customer organizations. These forward-deployed engineers, or FDEs, work hand-in-hand with customers to design and deliver AI solutions with specific goals.
At Northern Trust, the combined team — made up of FDEs and in-house finance experts — created a library of “rules” for the AI agent to follow.
The process can be tricky because different finance companies can use different terms for the same thing. “Everybody’s spreadsheet is different,” says Cherecwich. “But there are core things you can figure out: ‘Oh, this is a capital call. This is a valuation.’”
The teams grounded the model in business vocabulary and ran anonymized data from several previous onboardings — looking at how data was ultimately organized, which helped the model learn. Continuous feedback from the Northern Trust team helped expand the rule library.
“This was a true collaboration. Northern Trust brought a deep understanding of their business, and they looked to us for our AI expertise,” says Vik Singh, who leads the FDE team working on this project and is a corporate vice president at Microsoft Frontier Company. “We needed each other to get the agent’s accuracy right. I think it’s a model for how AI can move forward in regulated industries.”
“By using Microsoft’s FDEs, I got speed to market for something that might have otherwise been deprioritized.”
— Pete Cherecwich, chief operating officer of Northern Trust
The AI agent’s performance over time, or hill-climb in AI terms, has improved to hit a 96.5% FI first-pass quality score, a standard AI evaluation metric that combines precision and recall. The remaining 3.5% — stuff the AI agent is unsure how to categorize — is referred to a human to review.
This part is key; the Northern Trust team wanted to keep a human in the loop to avoid potential wrong information.
The company then proceeds with their regular reconciliation process — making sure their books and records match that of the previous administrator — before going live.
Using the AI agent has slashed the time needed to map transaction data from new clients from two and a half weeks to three days.*
“By using Microsoft’s FDEs, I got speed to market for something that might have otherwise been deprioritized,” says Cherecwich. Now, “speed to revenue is much faster. The client’s happier.”
Speed of change
All client data stays within Northern Trust and isn’t used to train any outside AI models. The bank is now looking at how AI agents can be used with other products and services.
Northern Trust has laid the ground for using more AI, offering training to all 23,000 employees on Microsoft Copilot. Employees have created over 15,000 Copilot agents to do things such as dig out specific data points from company prospectuses. To spur interest, the bank recently ran a contest with a cash award for best Copilot agent.
They’re also addressing employees’ questions. “Somebody asked me, ‘What does this mean for jobs?’” Cherecwich says. “My answer was: One of my first jobs was looking for municipal bonds on microfiche. Nobody is looking at microfiche anymore. So OK, jobs will change. You have to learn new skills.”
And while some things are changing fast, others will stay the same.
“We can be cutting edge if it’s low risk,” he says, adding: “We are not going to have AI do $100-million payments on its own anytime soon.”
Footnote*: Following deployment of the client onboarding agent, Northern Trust measured preparation times for real client onboarding scenarios. The analysis included data transformation, analyst review, exception handling, and final validation. The resulting improvement was calculated by comparing the prior workflow baseline of approximately 2.5 weeks with the average time required to complete measured production cases using the agent-assisted process.
Top photo: Pete Cherecwich, chief operating officer of Northern Trust, stands in front of the bank’s headquarters in Chicago.
Second photo: Pete Cherecwich, chief operating officer of Northern Trust, at work inside the bank’s Chicago headquarters.
(All photos by Alex Garcia for Microsoft.)
Chen May Yee writes about AI and innovation for Microsoft. Previously, she was a staff correspondent for Agence France-Presse and the Wall Street Journal and an editor at the Singapore Straits Times and the Minnesota Star Tribune, covering topics from business to health to technology. You can contact May Yee on LinkedIn.