Paymentus Says Customers Don’t Care How Much Data You Have

Watch more: What’s Next in Payments With Paymentus’ Chris Trainor

Data has become pervasive across the digital economy. What distinguishes companies is not whether they possess data, but whether they can use it with precision and immediacy to reduce friction and improve service.

In an interview for the PYMNTS “What’s Next in Payments” series on the evolving “data game,” Chris Trainor, head of platform strategy and innovation at Paymentus, argued that most firms have already mastered what might be termed mechanics.

As he put it, “most companies are now very good at collecting data. That’s no longer a differentiator.”

The firms that are advancing ahead of their peers are not assembling larger datasets, but are instead determining how to connect, interpret and deploy information while an interaction is still underway.

Trainor described three elements that separate leaders from the rest: unifying data across systems, understanding context and acting on that information without delay. “Data is no longer something you analyze after the fact,” he told PYMNTS. “It’s something that drives the interaction in real time.”

AI Compresses the Decision Cycle

Artificial intelligence (AI) is accelerating this transition by removing the lag in data-driven decision making. Historically, organizations relied on staged processes. Data moved through pipelines, analysts produced reports and decisions followed with delay.

“AI is collapsing the time between data, insight and action,” Trainor said, noting that what once required hours or days can now occur within a single interaction.

The implications extend beyond speed. AI reduces reliance on manual review and static rule sets, while improving decision quality through the inclusion of broader context. Instead of relying on isolated signals, systems can evaluate identity, behavioral history and transaction patterns simultaneously.

The result, in Trainor’s words, is “better decisions, made faster, with less friction.”

This compression of the decision cycle alters the economics of payments, fraud prevention and customer engagement. It also raises expectations. Customers no longer tolerate delays between intent and outcome, particularly when digital systems have the capacity to respond instantly.

Personalization as Competitive Advantage

“The biggest impact is removing friction and increasing relevance at the exact moment of interaction,” he said.

The impact is felt in the form of payment options tailored to the customer, approvals that reflect current behavior rather than static profiles and interfaces that respond to intent without requiring navigation across multiple systems. Customers, he said, prefer to “state intent and get an outcome,” rather than move through fragmented processes.

In payments and underwriting, this means decisions are no longer anchored to a single snapshot in time. They become dynamic, informed by continuous data flows. Conversion improves when interactions align with the customer’s circumstances in that moment, rather than relying on generalized assumptions.

Breaking Down Silos

Despite the promise, executing on this model presents operational challenges. Data remains dispersed across systems, often separated by function or legacy architecture. Payments data, identity records and behavioral signals are frequently stored in isolation.

Trainor emphasized that these divisions must be addressed directly. “It can no longer sit in silos. It needs to be connected,” he said.

The requirement, therefore, is two-fold: integrate data sources and interpret them in context, then act without hesitation. Each step depends on the others. Without integration, context is incomplete. Without context, action is misdirected. Without action, the data holds little practical value.

Identity sits at the center of this framework. Traditional fraud systems often treat each transaction as a discrete event, evaluating risk in isolation. That approach introduces friction and limits accuracy.

Trainor described a shift toward persistent identity models. “They’re not just asking, ‘is this transaction risky?’They’re asking ‘who is the user, what is their history, what does normal behavior look like for them,’” he said.

This perspective enables a more balanced approach to risk. Scrutiny can be intensified where anomalies appear, while trusted users encounter fewer obstacles. Fraud prevention improves not through additional checks, but through deeper understanding.

The effect extends to customer experience. When identity is established and context is clear, interactions proceed without repetition or delay. The system recognizes the user and adapts accordingly.

Companies that treat data as an operating system, rather than a static asset, are positioning themselves to lead.

Trainor framed the stakes in direct terms: “Winning the data game is not about collecting more data … it’s about controlling identity, context and execution.”