With this collaboration, the companies will help eligible enterprise finance teams modernize manual and “fragmented” B2B receivables processes where available, Fiserv said in a Wednesday (Aug. 4) news release.
“Businesses are increasingly looking for ways to improve customer experiences while optimizing working capital,” Jackson McIntosh, senior vice president for payments value added services at Fiserv, said in the release.
“Together with Stuut, we are combining our payment and receivables expertise with AI innovation, helping our clients streamline order-to-cash workflows, support productivity gains, improve operational efficiency and deliver greater value.”
Founded in 2024, Stuut helps B2B enterprises use agentic artificial intelligence (AI) to transform AI to transform “manual, error-prone” order-to-cash processes. The release said the company’s AI agent has collected more than $2 billion in B2B invoices.
With this collaboration, the companies will join Fiserv’s Commerce Hub, global payments platform and SnapPay order-to-cash solution with Stuut’s AI-enabled automation capabilities.
This combination is designed to “support collections, cash application, payments, disputes, and deductions, subject to applicable requirements and implementation timelines,” the release said.
Commerce Hub will act as the payment processing foundation for Stuut’s platform, while SnapPay will embed Stuut’s technology to, where available, help automate accounts receivable and B2B payment workflows for eligible enterprises.
PYMNTS wrote earlier this year about the “AI upgrade” happening to accounts receivable (AR) departments, a change that is “about what the systems now know” rather than just speed.
“Traditional AR reporting works as a lagging indicator: Teams compile aging reports at month’s end, categorize overdue invoices by days past due, and assess risk using historical averages or static credit scores,” that report said.
AI models integrated with enterprise resource planning (ERP) systems can now predict the likelihood of a specific invoice being paid late before it goes out, employing structured data such as payment history and invoice size, as well as unstructured data like sentiment from customer emails and dispute frequency.
“Purpose-built AR platforms take this further by applying a behavioral layer on top of ERP data. Rather than treating each transaction as an isolated event, these systems incorporate historical payment patterns to guide next steps automatically,” the report added. “A short payment is typically recorded as a variance in an ERP, an exception to be investigated.”
A purpose-built AR platform applies contextual intelligence: It understands the customer’s behavior and keeps the cash flowing, Lee An Schommer, chief product officer for Billtrust, said in an interview with PYMNTS.
