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AI is steadily becoming part of the everyday toolkit for finance teams, enabling more proactive decisions and deeper insight. At WEX, we see first-hand how this transition is unfolding 鈥� and how organizations can use AI to help transform reactive finance into forward-looking strategy.

In a recent, Eric Frankovic, President of Corporate Payments at WEX, discussed how AI is reshaping cash flow, risk management, supplier relations, and more.

From reactive to proactive management

Historically, finance teams have operated in 鈥渓ook-back鈥� mode, analyzing past performance and building forecasts based on comparison. That model is under strain. Eric describes how 鈥淎I gives [CFOs] cash flow management in a really active sense 鈥� real-time visibility, actively spotting trends and risks as they happen.鈥�

With AI, decisions no longer wait on end-of-month reports or quarterly reviews. The signals come in continuously, enabling faster responses to liquidity shifts, supplier stress, or unexpected costs.

鈥淵ou don鈥檛 have to wait for reports. You don鈥檛 have to depend on sort of a static snapshot that occurred in the past. These signals are coming in real time and you can make decisions based off them.鈥�

That鈥檚 what Eric refers to as 鈥渁ctive cash flow management鈥� 鈥� transitioning finance from a rearview mirror role to one that uses real-time data to guide strategic decisions.

Automating the mundane, elevating the strategic

Part of AI鈥檚 appeal is its ability to absorb the low-value, high-volume tasks that bog down teams. This can mean tasks like matching transactions, reconciling ledger inconsistencies, spotting anomalies, etc. These chores can now happen in the background, freeing up human talent for higher-level work.

鈥淲hat we鈥檙e seeing now is monitoring and automating manual checks, matching transactions faster than ever before,鈥� Eric explains. 鈥淔lagging 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.鈥�

Detecting patterns, minimizing risk

Automating certain tasks is quickly becoming a necessity. Manual processes, especially in fraud detection, cannot scale at that level without introducing delays and risk. 鈥淎I-based anomaly detection learns from patterns over time, not just fixed rules. It adapts to new tactics from fraudsters and flags unusual behaviors earlier and more accurately.鈥� Eric says. Ultimately, as fraudsters use AI to become more sophisticated, our strategies to stop them must evolve as well.

But AI doesn鈥檛 replace human judgment; it augments it. In situations with nuance or unclear boundaries, finance leaders still need to intervene to assess the situation and have the final say. The more AI handles routine tasks, the more human expertise is reserved for the gray areas.

Strengthening the buyer鈥搒upplier relationship

Another front where AI has potential is in rebalancing how buyers and suppliers interact. 

鈥淲e鈥檝e been talking for years about working capital management where dynamic discounting was at play. I think one of the things that I鈥檓 really excited about is that next evolution, which is how do we make the buyer-supplier relationship strong and sticky.鈥�

Nobody expects a perfect AI tomorrow. But in finance especially, being early means having more room to learn, adapt, and shape advantage. Leveraging tools like AI and automation may pay off in the long run and result in shared value for the buyer and supplier, beyond just the transaction.

For more insights and updates on corporate payments, check out:

• Your guide to virtual card rebates
• 2026 payment trends
• Is the secret to better cash flow management AI?

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The information in this blog post is for educational purposes only. It is not legal, tax or investment advice. For legal, tax or investment advice, you should consult your own legal counsel, tax, and investment advisers.