The enterprise data game has evolved. It’s no longer what firms have and how much, but what they do with it and how those applications can scale.
“It’s about whether the data that you collect actually captures real context and real intent,” Dewald Nolte, co-founder and chief strategy officer at Entersekt, told PYMNTS during a discussion for the April edition of the “What’s Next in Payments” series, “The Data Game.”
That shift from data accumulation to data intelligence is already reshaping how financial institutions think about authentication, risk, and ultimately, trust.
The companies pulling ahead are those extracting the most meaning from the data they collect by turning fragmented signals into decisioning frameworks that can balance operational concerns, like fraud prevention with seamless customer experience.
“The big difference between companies that use data well and those that struggle comes down to how they actually leverage the data that they’ve captured and turn it into actionable outcomes, in real time,” Nolte said.
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Why Data Context Matters More Than Volume
For years, financial institutions focused on expanding their data footprint, operating under the assumption that more inputs would yield better outcomes. But the explosion of artificial intelligence has exposed a critical flaw in that logic: without quality and context, data becomes noise.
At the same time, there’s a growing emphasis on immediacy. Payments is now a millisecond decisioning environment where fraud detection, authentication and approval must happen simultaneously.
“If you build silos where a system only has a view of online banking and it doesn’t look at mobile banking or call center data, those are siloed system views,” Nolte said. “Then it’s very easy for a fraudster to attack across those channels.”
The only effective countermeasure is a unified, real-time understanding of behavior across every touchpoint. And despite the promise of technology, even advanced AI systems will fail when fed flawed inputs. Without accurate context, automation amplifies errors instead of reducing them.
“Garbage in, garbage out. If you’re capturing poor quality data, you’re going to make the wrong decision,” Nolte said.
“In a connected world, building silos will get you in trouble,” he added.
That’s also where AI can help. The technology can be valuable when deployed to unify signals across channels, breaking down long-standing silos between systems.
“If you’re able to collect data across different channels, the speed at which you can look at the data and make sense of it is so much more powerful,” Nolte said. “What was relatively difficult or very slow just a couple of months ago, you can do relatively quickly right now.”
Bots, Agents and the New Risk Model
Complicating this is the rise of autonomous agents and sophisticated bots, which can blur the line between valid users and fraudsters, especially as consumers start to delegate tasks like shopping, payments and even account management to digital agents.
“Not too long ago, bots were a surefire signal that something bad is happening. Now, it’s not necessarily something bad,” Nolte said.
The challenge is determining intent: whether an action aligns with established patterns or deviates in meaningful ways. That can require new signals, including explicit mandates. Did the customer authorize an agent? Under what conditions?
“You have to understand the buying behavior, what they typically buy, what they typically use,” Nolte said. “Those are signals that we have to collect at the right point so that when the transaction comes in, we have the ability to actually authenticate against that mandate.”
Much of this complexity is invisible to the end user, but it’s all aimed at their benefit. After all, few experiences are more frustrating than being blocked during a legitimate transaction. In a competitive world, friction directly affects conversion and loyalty.
“Data plays a very important role in making sure that returning good customers are not inconvenienced,” Nolte said.
As the payments ecosystem recalibrates around data intelligence, competitive advantage is shifting from infrastructure to execution. For Nolte, winning the “data game” over the next 12 to 24 months comes down to three priorities.
First, invest in meaningful data, not just more data.
“Capturing data that actually captures real-world behavior and context, that’s going to be very important,” he said.
Second, eliminate silos to enable real-time, cross-channel decisioning.
And third, operationalize AI as a decision engine.
“Using AI to turn data into real-time, customer-friendly outcomes, that’s what winning looks like,” Nolte said, noting that as AI tools become more commoditized, what separates leaders is how effectively they apply them.
Because in payments, the future won’t be decided by who has the most data, but by who understands it best, fastest and in context.
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