Agentic AI moves into accounts receivable as firms target late payment risk

The agentic era has arrived, and it’s rewriting cash flow rules 

By Dave Ruda, VP Product at Billtrust 

“We were struggling with spreadsheets. We couldn’t keep track of all the customers we had to reach out to, because we were sending every payment reminder email manually.” 

That’s a direct quote from one of our customers and represents a common sentiment caused by an incredibly common and costly blindspot. For an average mid-market business, late payments translate to $20m tied up in open balances and $500k lost annually in bad debt. It’s a stark reminder that traditional Robotic Process Automation (RPA) is no longer sufficient to counteract the triple threat of systemic payment delays, rising operational costs, and chronic staffing shortages. And unfortunately, this customer wasn’t alone. Our research shows payment delays continue to be a major challenge for 81% of firms.

To survive this pressure, the industry is undergoing a massive, overdue structural move to agentic finance. The market is tired of basic AI tools that merely rephrase emails or compile notes. Today, specialised autonomous agents move beyond simple data summarisation to execute complex, end-to-end financial logic, including real-time, automated credit limit changes and intelligent outreach orchestration. 

If you’re a CFO, this is a very welcome shift. Now, you have a clear blueprint to become a governor of an AI ecosystem underpinned by a new, auditable framework that prevents revenue leakage before it occurs.

From generative assistance to agentic autonomy 

To make good on this blueprint, we must look closely at what autonomy actually means in a modern finance department. A year ago, CFOs used basic tools that suggested email drafts or summarised some data. Today, that isn’t enough. Thankfully the tech has evolved to use autonomous agents that analyse behavior patterns across trillions of transactions and are able to determine the optimal channel and timing for dispute resolution without getting stuck in an inbox. 

Consider the difference between a passive tool and an active digital team member. A passive AI tool drafts an email response to a billing dispute that a human still has to review, double-check and manually click send. An autonomous agent, however, notices a payment mismatch, automatically cross-references receipts, identifies the root cause for the dispute and then contacts the buyer at the precise moment they are likely to respond. This acts more as a digital team member executing financial workflows from start to finish. 

Turning credit into a real-time revenue engine

With 55% of B2B sales now arriving late, static credit limits stand as an active liability. Reviewing a customer’s creditworthiness once a quarter is an unacceptable risk. This legacy approach forces finance teams into a corner: they either take on unmitigated risk by leaving limits too high or they damage sales velocity by keeping limits too low. Business can no longer afford this defensive stance, which is why the shift toward immediate, flexible cash flow management is non-negotiable. 

Instead of waiting for quarterly reviews, modern platforms deploy automated, real-time credit adjustments driven by live, continuous data on buyer risk and payment behaviour. This transforms credit from a defensive gatekeeper into a high-velocity revenue engine. Algorithms instantly trigger limit increases to capture opportunities the moment a buyer’s reliability is verified. If a customer suddenly receives a massive influx of orders and needs more buying power, an agentic system instantly verifies their recent payment consistency and adjusts their credit line upward within minutes. Sales are captured immediately, friction is eliminated and the business expands safely without manual credit department bottlenecks. A true win-win. 

Navigating governance 

Trust is now the ultimate operational hurdle as AI moves from a passive back-office advisor to an independent front-office actor. The black box nature of autonomous systems is being challenged by regulators, particularly as the stringent new EU AI Act clamps down on automated financial decisions. Regulators and corporate boards are demanding total transparency, and rightfully so. If an AI system makes a decision that impacts revenue or customer relationships, businesses can’t pass the blame to an unexplainable algorithm.

The only viable path forward is transparent, explainable accounts receivable (AR). This framework ensures that every autonomous decision, whether it is a sudden credit denial or a decision to escalate a client to collections, generates a clear, step-by-step rationale that a human supervisor can easily audit and understand. 

Consider how this plays out in real life: if an AI agent reduces a long-standing buyer’s credit limit, the system will surface the exact data points that triggered the decision. These data points could include a localised macro-economic dip in the buyer’s region paired with a 15-day deceleration in their last three payments for example. This level of clarity completely reframes the compliance conversation and guarantees that human oversight remains a meaningful, legally compliant safeguard.

Prioritising automated prevention 

Data-driven interventions work. Our research shows a 25% reduction in days sales outstanding (DSO) is possible through early intervention. To avoid chasing a bad debt 30 days later, an agentic ecosystem creates a frictionless environment that starts to mitigate late payment risk the moment an invoice is issued. Forward-looking companies now have the ability to flag potential payment hurdles before they occur, analysing invoice accuracy, historical behaviour and billing configurations to ensure a clean delivery. 

CFOs are stepping into the role of strategic governors. Armed with transparent AI workflows and real-time risk data, they are transitioning from defensive process overseers to architects of an automated, self-correcting financial ecosystem. This shift will free the finance suite from the endless cycle of chasing payments, transforming its function into a proactive growth driver.