Real-Time Rails Put Bank Data to the Test

In payments, data is no longer the advantage. Real-time action is, particularly as artificial intelligence (AI) narrows the gap between insight and execution, embedding decision-making directly into every payment flow.

“Data is moving from a passive asset to an active asset,” Deepak Gupta, chief product, engineering and delivery officer at Volante Technologies, told PYMNTS during a discussion for the April edition of the “What’s Next in Payments” series, “The Data Game.”

“Most banks have terabytes and terabytes of data,” Gupta added, “but what they’re unable to do is act on that data in real time.”

That lag is becoming a millstone around banks’ necks. Payments have become both faster and more complex, and expectations for immediacy now extend beyond speed to certainty. Banks that can operationalize data in-flight, across every rail and with governed autonomy, are already proving to be more capable of delivering faster, cheaper and more predictable outcomes.

Those that don’t may find that in standing still, they’ve missed the window to close the competitive gap.

The Liability of Fragmentation

One of the most significant barriers to achieving real-time decisioning is legacy fragmentation. Most banks operate multiple payment systems, each aligned to a specific rail — ACH, wires, cards, real-time payments — often with distinct data models, processing engines and operational workflows.

“You shouldn’t have to learn the pattern again and again, depending on the rail,” Gupta said. A unified platform, he added, can allows insights — and defenses — to propagate across all payment types.

The data fragmentation across banking also creates silos, with implications that are becoming particularly visible in the industry’s migration to ISO 20022, a global messaging standard that expands the amount of structured data embedded in each transaction. Many institutions have achieved compliance — meaning they can send and receive messages in the new format — but few have translated that into advantage.

“The difference is, are you compliant or are you competitive?” Gupta said. “Are you able to convert that data compliance into a competitive advantage, or not?”

AI-driven systems can help invert the fragmented legacy model. Instead of treating things like payment exceptions as a separate workflow, they integrate decisioning into the core processing layer. The system then continuously evaluates every transaction, applying adaptive logic that evolves over time.

At Volante, for example, Gupta said this transformation is being operationalized through agent-based AI models embedded directly into payment workflows. He described four categories of systems that: prevent errors before they occur, repair failed transactions, predict optimal routing paths and sense emerging system risks. The common thread is autonomy calibrated by confidence.

“AI is basically accelerating the shift from analysis to real time execution,” Gupta said, noting that tasks that once required human intervention, such as investigating failed payments, identifying anomalies and correcting data, can increasingly be handled automatically.

“You’re going from a human driven system to an autonomy-based system,” he added, though noting the importance of strict governance boundaries.

Predictability as the New Competitive Edge

Speed has long been a proxy for competitiveness in payments. Faster processing times, quicker settlement and reduced latency have been key differentiators. But speed alone is no longer sufficient. As real-time payments become ubiquitous, the baseline expectation is instant execution. The differentiator is what happens within that instant.

Fraud detection is one of the clearest beneficiaries of this real-time approach. Traditional systems relied on static rules and thresholds, generating high false positives and requiring constant manual tuning. AI, by contrast, enables adaptive, behavior-driven models that evolve continuously.

“Banks are moving from static rules to adaptive data-driven decisioning,” Gupta said. Detection is no longer a checkpoint at the beginning of a transaction, but a continuous process across its lifecycle.

The downstream effect is not just efficiency, but predictability, an attribute Gupta repeatedly emphasized as underappreciated. A payment that completes in seconds but fails unpredictably may be less valuable than one that consistently settles within a known timeframe. In high-stakes scenarios from corporate treasury to real estate closings, that reliability becomes a core part of the customer experience.

“It’s not just about speed; it’s about predictability of payment processing,” Gupta said, citing cases where straight-through processing rates have risen from 20% to 95%, effectively eliminating manual intervention for the majority of transactions.

In that context, data is no longer just an input. It is the operating system. And as Gupta makes clear, the institutions that win will not be those that simply collect more of it, but those that can act on it — instantly, intelligently and everywhere it matters.