Why Your AR Team Could Predict Your Next Liquidity Crisis

Watch more: Wrapping AI Around Legacy AR Shifts Decisioning Into Machine Speed

Volatility, high interest rates and persistent pressure on liquidity are turning back-office functions into boardroom concerns. Perhaps nowhere is this shift more apparent than in accounts receivable (AR).

Historically, AR departments functioned primarily as bill collectors: issuing invoices, chasing late payments and managing exception workflows. Automation, where it existed, focused on digitizing the invoice lifecycle and improving efficiency.

But thanks to advances in artificial intelligence (AI), predictive analytics and enterprise data integration, what was once a transactional cost center is now being recast as a forward-looking engine of working capital optimization, risk mitigation and customer experience management.

“AR is no longer about settling the past. It’s about predicting the future of cash,” Pamela Novoa Ralli, head of product management at FIS, told PYMNTS. “It’s moving from a responsive to a proactive view.”

After all, in today’s environment, having visibility into future cash flow is no longer a luxury but frequently a requirement.

“AI allows predictability features to be at the core of the AR solution, instead of what we have seen in the last 10 years, which is about looking at the past and focusing on efficiency and effectiveness of the current state,” Novoa Ralli said.

By applying machine learning models to historical payment data, behavioral signals and macroeconomic variables, modern AR systems can generate highly accurate forecasts of days sales outstanding (DSO), flag early signs of customer distress and even recommend optimal follow-up strategies based on real-time conditions.

Of course, while the potential of artificial intelligence in AR is tantalizing, achieving full autonomy is a complex journey.

Read the report: AI Power: The Technology Transforming Accounts Receivable

Navigating the Road to AI-Powered AR Autonomy

Perhaps the most important, and misunderstood, aspect of AI in AR is how it integrates with existing infrastructure. Contrary to fears of expensive rip-and-replace mandates, modern AI solutions are typically designed to surround rather than supplant legacy ERP and CRM systems.

“It’s not about ripping out your existing systems. It’s about wrapping them with intelligence,” Novoa Ralli said.

That “intelligent wrapper” often takes the form of cloud-based AI layers that plug into existing platforms via APIs, ingesting data from multiple sources and delivering insights back into the systems finance teams already use.

This approach reduces time-to-value, mitigates risk and allows companies to incrementally scale their AI adoption. But successful implementation still requires investment in three foundational enablers: data governance, change management and cross-functional collaboration.

“We see a much more educated AR user that understands data management … and is then able to drive cross-functional collaboration around AI deployment,” Novoa Ralli said. “Companies are investing a lot in data governance. That is the first step toward anything related to AI.”

“True autonomy isn’t about fewer humans … it’s about better judgment at machine speed,” she said.

As for how to get there, six key milestones can help guide the way toward next-generation AR solutions. The first is specialization.

“What you see a lot [of] right now is generic agents,” Novoa Ralli noted, stressing that next-generation AR solutions require agents specialized in specific domains such as credit, disputes, collections and reconciliation.

Second, when it comes to predictive analytics, trustworthy forecasts are key. While predictive analytics isn’t new, the emphasis now is on improving the reliability of AI-powered insights.

Third, self-service portals that operate as robust platforms enabling end-to-end user autonomy will be a crucial part of the user architecture.

Autonomous credit management, continuous learning and embedded compliance round out the six crucial modernization markers.

“Compliance is evolving in front of our eyes … the ability for the system to self-regulate and to educate the user on their data management is also going to be critical,” Novoa Ralli said.

The Future of AR Is Relationship Intelligence at Scale

Nowhere is the impact of artificial intelligence more visible than in dispute resolution. Traditionally, companies managed disputes reactively: waiting for customer complaints, then launching time-consuming investigations.

With AI, this process is being inverted. By analyzing patterns in disputes over time, AI models can predict which customers or invoice types are most likely to trigger issues. Some systems now go a step further, suggesting proactive outreach or invoice adjustments to preempt conflict entirely.

“Every dispute is a data point,” Novoa Ralli said.

The result: lower dispute volumes, faster resolution times and improved customer satisfaction. In industries with complex billing processes — like telecommunications, manufacturing or logistics — these improvements can translate to millions in recovered revenue and reduced churn.

Credit management, too, is undergoing a reinvention. While traditional credit scoring models have long used static datasets and opaque methodologies, AI is enabling a more nuanced and transparent approach.

For its part, FIS is piloting the concept of a “trust score” — an explainable, auditable model that not only generates a risk score but shows users how it arrived at that conclusion. The goal is to enhance both the accuracy and credibility of credit assessments.

“Our core value is: can you show me the data?” Novoa Ralli said. “We want systems that tell you the answer and also how they got there.”

Want to know the best time to call a customer about an upcoming invoice? The system will tell you — based on payment patterns, calendar activity, and behavioral signals. Wondering whether a one-time delay signals risk? The AI will assess it in context.

“We’re going to see a lot more intelligence around relationship management and total value of the relationship and not just invoice-level transactions,” said Novoa Ralli, pointing to the fact that the AR function is increasingly no longer just about collecting cash but about managing risk, optimizing liquidity and deepening customer relationships.

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