Billtrust Debuts Agentic Credit to Catch Payment Risks
Billtrust is turning to agentic artificial intelligence (AI) to help finance teams spot payment risks.
The company, which makes B2B accounts receivable (AR) workflow and payment software, announced the launch of its Agentic Credit Lines product on Tuesday (March 3).
The new offering, embedded into the company’s credit review workflow, “analyzes payment history, utilization patterns, and external credit data using Billtrust’s proprietary network of 13 million buyers and 25 years of B2B payment intelligence,” the company said in a news release.
From there, the tool can deliver credit limit recommendations with transparent rationale, giving finance teams more visibility, stronger portfolio control and the ability to detect risk sooner.
“As AI reshapes financial operations, finance leaders need tools that enhance, rather than replace, human judgment, and Agentic Credit Lines delivers with transparent, data‑driven recommendations that let teams act quickly while maintaining full oversight and compliance,” Billtrust said in the release.
The company said this approach lets finance teams address the visibility, efficiency and risk management challenges such as spotting payment issues before they arise, reducing manual revenue effort and ensuring compliance.
“It’s time to stop reacting to risk and start getting ahead of it with technology that elevates human judgment instead of replacing it,” added Lee An Schommer, chief product officer at Billtrust. “Agentic Credit Lines represents the next step in our evolution from workflow automation to intelligent engagement.”
PYMNTS spoke earlier this year with Michael Younkie, vice president of product management at Billtrust, about the challenges that come with automating AR operations.
When done well, that report said, the impact of this automation can be quickly felt. Companies see increased straight-through processing in their cash application processes while invoicing and collections realize faster digital adoption and quicker payments.
“Customers often see measurable improvements in their system 30 to 90 days after go live,” Younkie said.
And as PYMNTS added, the “levers that accelerate time to value are pragmatic rather than flashy: clean, standardized data, process alignment before configuration, focused role-specific training and phased deployments that target high-impact areas first.”
All the same, data readiness remains a sore point for many companies, with some expecting AR automation platforms to be an automatic fix for underlying data issues. Younkie pushed back against that belief, while pointing to the role automation can play.
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