Bmo Uses Codat AI to Scale Mid-Market Sales Intelligence
Bmo is using Codat’s machine learning and API connections to analyze customer payments and accounts payable data for its mid-market business relationships. The bank moved from bespoke work that Rose Grande said could not scale to a system meant to support Treasury and payment sales teams more consistently.
Rose Grande on Bmo scale
“We’re able to do customer intelligence work ourselves on a very bespoke, one-off basis, but when it came to being able to scale it, we weren’t able to do so,” said Grande, head of North American corporate card product and programs at Montreal-based BMO. That shift matters for business clients because the sales side now gets recommendations tied to customer behavior rather than relying on individual manual analysis.
Codat pulls payments and accounts payable information through application programming interfaces and retrieves specifics about which suppliers customers are paying and how they are paying them. It then uses machine learning to generate recommendations for BMO’s salespeople, giving them a more structured view of business-client activity before conversations begin.
Codat connects more than 20 systems
More than 20 enterprise resource planning and accounting software programs feed the system through direct API connections, including QuickBooks, Oracle, NetSuite, Sage, Microsoft Dynamics, Workday and Xero. When Codat has not built an API for a system, it uses an intelligent upload tool so customers can upload raw files.
The vendor categorizes transaction files and shares personalized recommendations with BMO’s Treasury and payment sales teams. Those teams then use the data to have better conversations with customers, a setup Grande said lets the bank play the role of trusted advisor in a more consistent way.
AI becomes a bank priority
66% of bankers said AI is a strategic priority for the firm in American Banker’s AI Talent Shift survey this year, a backdrop that fits BMO’s move to make the technology part of client-facing sales work rather than only an internal efficiency tool. Bradley Leimer, founder and principal of Leimer One Advisors, said banks want to use AI not simply to automate internal tasks, but to better understand client behavior, identify any unmet needs and make bank relationship managers more effective.
“Getting customers to share data is an innate pain point that we see all over banking,” said Joey Rault, chief revenue officer at Codat. For BMO, that friction is the operational hurdle inside the new system: the bank can now scale customer intelligence, but only after customers provide the payment and accounts payable data that feeds the recommendations.
“We like to be that customer’s trusted advisor, and this allows us to play that role in a more consistent way,” Grande said. For mid-market clients, the practical change is simple: the bank’s sales teams are no longer limited to one-off analysis, and the advice they receive is now tied to a repeatable data pipeline built around payments, suppliers and accounting systems.
