AI Helps CFOs Unlock Future Value Sooner

Hindsight is 20/20, which made it perfect for the world of corporate finance. After all, cash flow management has long been an exercise in hindsight and certainty.

Chief financial officers traditionally pored over weekly or monthly reports, using historical data to make forward-looking decisions. And while this approach often left back-office leaders chasing problems rather than staying ahead of them, the rise of artificial intelligence is rapidly changing that equation.

“AI gives [CFOs] cash flow management in a really active sense — real-time visibility, actively spotting trends and risks as they happen,” Eric Frankovic, president of Corporate Payments at WEX, told PYMNTS during a discussion for the B2B PYMNTS 2025 event, “B2B.AI: The Architecture of Intelligent Money Movement.”

In today’s hyper-connected business environment, the idea of reacting to last week’s numbers already feels archaic for forward-thinking leaders.

“You don’t have to wait for reports. You don’t have to depend on sort of a static snapshot that occurred in the past,” Frankovic said. “These signals are coming in real time and you can make decisions based off them.”

His emphasis on “active cash flow management” encapsulates a pivotal shift in the industry. By drawing on live data from invoice statuses, payment authorizations, shipment milestones and other signals, finance teams can forecast shortfalls, identify risks and make decisions on the fly.

This transition is increasingly moving finance teams from playing defense to playing offense.

“What we’re seeing now is monitoring and automating manual checks, matching transactions faster than ever before,” Frankovic said. “Flagging the issues that are cropping up in real time cuts down on manual errors and speeds up closing at the month end. And it helps finance teams focus on higher-value work.”

How AI Is Giving Real-Time Finance a Shot in the Arm

Real-time decision-making has particular resonance in payments, which is a domain where delays, mismatched ledgers or fraud can cascade quickly into liquidity issues or supplier strain. In this new landscape, artificial intelligence is not merely a tool for efficiency; it can be a strategic necessity. AI’s ability to sift through massive data flows, detect patterns and flag anomalies before humans can is giving finance teams an unprecedented early-warning system.

“AI, overall, what it’s doing, it’s spotting patterns,” Frankovic said. “In this case, it’s spotting patterns in payment behaviors like delays or spending trends that a human has access to but might miss or might not put together.”

In a large organization, a single finance manager might only see part of the picture: delayed payments in one department or an uptick in supplier charges in another. AI can integrate these signals across the enterprise, flagging emerging liquidity gaps, slowing collections, rising expenses or supplier risk.

“All of these things were available always,” Frankovic said, “but what AI does is it gives the real-time ability to coordinate all of them into one message, then make decisions off those real-time messages.”

In an era defined by supply chain volatility and inflationary pressures, these early warnings aren’t just valuable. They can be essential for survival.

But while the allure of AI-driven efficiencies is strong, Frankovic also emphasized the importance of sustainable growth, noting that that trust and relationships between enterprises, their suppliers and their financial partners are as critical as technology.

AI Unlocks New Benefits by Piggybacking on Payments Volume

For its part, WEX processes hundreds of thousands of transactions every second. At that velocity, traditional fraud detection — built on static rules and human reviews — can’t keep up. AI’s adaptive capabilities, particularly in anomaly detection, have become critical.

While AI hasn’t eradicated fraud (“They have access to some of these same tools,” Frankovic cautioned), it has tilted the playing field in favor of defenders. At least for now. The race is ongoing, and success depends on continuous adaptation.

“AI-based anomaly detection learns from patterns over time, not just fixed rules,” Frankovic said. “It adapts to new tactics from fraudsters and flags unusual behaviors earlier and more accurately.”

For Frankovic, one of the most promising near-term applications of AI lies in the often-contentious buyer-supplier relationship. Historically, power has shifted back and forth between the two sides, first to buyers who dictated payment terms, then toward suppliers through mechanisms like dynamic discounting and supply chain finance.

“We’ve been talking for years about … working capital management where dynamic discounting was at play,” he said. “I think one of the things that I’m really excited about is that next evolution … which is how do we make the buyer-supplier relationship strong and sticky.”

He estimated that “we’re probably six to eight months away from really seeing Ferrari-status tools” in this space capable of analyzing factors such as industry cycles, macroeconomic conditions, and individual supplier needs.

The payoff, he said, will be healthier long-term relationships that move beyond transactional considerations to shared value creation.

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