Billtrust CEO Takes Aim at B2B’s Trillion-Dollar Cash Jam
Billtrust CEO Grant Halloran says nearly 60% of B2B invoices are overdue, trapping $1.5-2 trillion in cash. An AI-driven approach is shifting collections from reminders to decision systems.
Nearly 60% of B2B invoices are overdue, trapping $1.5 trillion to $2 trillion of what Billtrust CEO Grant Halloran calls “trapped cash” in the economy, he said in an interview with PYMNTS for the August 2026 edition of the “What’s Next in Payments” series.
“It’s physically messy,” Halloran said, describing the fragmented B2B payments landscape where buyers use different channels, suppliers operate on disparate systems, and checks persist alongside digital rails. The conventional response of automating pieces of the order-to-cash process has not solved the core problem, he said.
Halloran said AI-powered capabilities now allow firms to distinguish among buyers by behavior, economics, relationship value and likelihood of payment. That changes collections from a standardized sequence of reminders into a decision system. “Businesses are trying to generate the most cash possible from their receivables at the fastest rate and at the best economics,” he said.
He cautioned against forcing buyers into the cheapest payment rail. “You can’t just force buyers into the cheapest rail possible,” Halloran said, stressing that the receivables optimization problem is multidimensional: “generate cash faster, improve economics and preserve the customer relationship.”
The amount of data processed on the Billtrust platform is “quite profound,” Halloran said, with billions of behavioral and non-behavioral signals from a trillion dollars of trade credit commerce. The biggest inflection, he said, is “how do we enable the humans to be doing the most value-added work, in more strategic decisions, that directs the operational.”
Halloran’s comments come as elevated borrowing costs and rising days sales outstanding increase pressure on companies to convert receivables to cash more efficiently. The operating model may eventually invert, with machines handling repetitive decisions and humans focusing on exceptions where relationship or strategic judgment outweighs the algorithmically obvious answer, he said.
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