The enterprise data game has evolved. It’s no longer about what companies have and how much they have, but what they do with it and how these applications can scale.
“It’s about whether the data you’re collecting actually captures the real context and the real intent.” Dewald Nolteco-founder and chief strategy officer at Entersaid PYMNTS during a discussion of the April edition of “What’s next in payments?“Data Game” series.
This shift from data accumulation to data intelligence is already reshaping how financial institutions think about authentication, risk, and ultimately trust.
The companies that move forward are those that extract the most meaning from the data they collect by transforming fragmented signals into decision frameworks that can balance operational concerns, such as fraud prevention, with a seamless customer experience.
“The big difference between companies that use data well and those that struggle is how they actually leverage the data they’ve got and turn it into actionable results, in real time,” Nolte said.
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Why data context matters more than size
For years, financial institutions have focused on expanding their data footprint, operating on the assumption that more input would lead to better outcomes. But the explosion of artificial intelligence has exposed a serious flaw in this logic: without quality and context, data becomes noise.
At the same time, there is an increasing emphasis on speed. Payments are now a millisecond decision environment where fraud detection, authentication and approval must happen simultaneously.
“If you build silos where the system only has a view of online banking and doesn’t look at mobile banking or call center data, those are siled views of the system,” Nolte said. “It is then very easy for a fraudster to attack through those channels.”
The only effective countermeasure is a unified, real-time understanding of behavior across every touchpoint. Despite the promise of the technology, even advanced AI systems will fail when fed flawed inputs. Without accurate context, automation amplifies errors rather than reduces 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 into trouble,” he added.
This is also where AI can help. This technology can be valuable when deployed to standardize signals across channels, breaking down long-standing silos between systems.
“If you are able to collect data across different channels, the speed at which you can look at the data and understand it will be much stronger,” Nolte said. “What was relatively difficult or very slow just a couple of months ago, you can do relatively quickly now.”
Robots, agents and the new risk model
Complicating matters further is the rise of independent agents and sophisticated bots, which can blur the line between legitimate users and fraudsters, especially when consumers start delegating tasks like shopping, payments and even account management to digital agents.
“Not long ago, robots were a sure sign that something bad was going on,” Nolte said. “Now, it’s not necessarily a bad thing.”
The challenge is to determine intent: whether the action conforms to established patterns or deviates in meaningful ways. This may require new signals, including explicit mandates. Has the client authorized an agent? Under what circumstances?
“You have to understand purchasing behavior, what they typically buy, what they typically use,” Nolte said. “Those are the 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 authorization.”
Much of this complexity is invisible to the end user, but it’s all aimed at their benefit. After all, there are few experiences more frustrating than being blocked during a legitimate transaction. In a competitive world, friction directly impacts conversion and loyalty.
“Data plays a very important role in making sure good customers don’t get spammed,” Nolte said.
As the payments ecosystem resets around data intelligence, competitive advantage shifts from infrastructure to implementation. For Nolte, winning the “data game” over the next 12 to 24 months depends on three priorities.
First, invest in useful data, not just more data.
“Capturing data that actually captures real-world behavior and context is going to be very important,” he said.
Second, remove silos to enable real-time decision making across channels.
Third, activating artificial intelligence as a decision engine.
“Using AI to turn data into real-time, customer-friendly results is what winning looks like,” Nolte said, noting that as AI tools become more of a commodity, what differentiates leaders is how effectively they apply them.
Because in payments, the future will not be determined by who has the most data, but by who understands it best, fastest, and in context.
