Data Takes Fraud Spotlight as Banks Shift to Real-Time Operations

Watch more: AI Takes Command in Finance as Banks Shift to Real-Time Operations

Artificial intelligence has become a buzzword so pervasive in financial services that it may already be at risk of losing its punch.

Behind the hype, the fundamentals of how banks and FinTechs operate are being fundamentally reshaped.

This isn’t a wholesale rug-pull of core infrastructure. Banks and FinTechs have long used AI, even if they don’t always recognize it. Legacy systems built around credit scoring and risk modeling, for example, were early forms of machine learning.

“From my point of view, AI and machine learning have been integrated into these financial institutions for years … the term AI has broadened, though, so there’s more underneath that umbrella now,” Eric Stratman, senior director, analytics and insights, at ValidiFI, told PYMNTS.

What’s changed today isn’t just the tools, but the scale, speed and complexity of the problems they now address, he said.

“In the past, credit risk models were viewed as standalone systems,” Stratman said. “Today, machine learning is being used to identify fraud in real time, validate accounts instantly, and minimize transaction failures, all with little to no human input.”

Still, this new functionality also comes with its own challenges. Chief among them is trust.

“Some customers are hesitant about these results initially,” Stratman said. “But once they implement these solutions and start analyzing the outcomes, they quickly begin to realize the benefits … some of the bad accounts being identified by these methodologies have as high as a 90% return rate.”

One of the most visible use cases for AI in payments is account validation, especially with the rise of pay-by-bank systems that aim to replace credit cards in digital commerce. These systems are cheaper and more direct, but they require near-perfect accuracy to avoid costly transaction failures.

Traditional validation methods, like micro-deposits, are riddled with friction and dropout risk. Stratman called them “costly at first” and said they “introduce high friction in the process,” often causing consumers to abandon transactions.

By using AI to assess patterns in transactional behavior, firms like ValidiFI can confirm account validity instantly and non-invasively. This can result in higher approval rates and reduced payment friction, a small change with big downstream effects on customer experience and operational cost.

“This is where AI/ML can really shine,” Stratman said. “Our models analyze the patterns and the behavior of that account to validate whether it’s real or not.”

Validation is only half of the payments security equation. The other is fraud detection. Here too, AI is turning legacy processes on their head, which is a relief because fraud today is smarter, faster and increasingly automated, he said. Fraudsters are getting more sophisticated as their toolkit expands.

“Third-party fraud, or account takeover fraud … involves providing credible account information to game the system,” Stratman said. “But AI/ML can analyze that account behavior and transactional data in the background to identify if unusual behavior is occurring. If it is, it can stop that fraud in real time.”

For AI to fulfill its potential in banking, Stratman said the technology must be demystified. Many customers might hesitate to rely on machine-generated scores without understanding how they were created. Transparency, not just accuracy, becomes a differentiator in this context.

“It’s on us, on the product side, to provide enough detail and transparency into how we’re achieving that score and what it really represents,” Stratman said.

He also said he believes banks need more than results; they need explainability, especially in a regulatory environment that increasingly demands it.

At the same time, the most critical AI asset isn’t the algorithm but the data. Good models built on poor data are worse than useless; they’re misleading.

“AI is just the methodology,” Stratman said. “What’s really driving these solutions is the data behind the models. It’s very important that we put in quality data … so that when you scale, you’re not losing effectiveness.”

To that end, ValidiFI continually analyzes its data consortium, a shared pool of anonymized transactional data that reveals broader consumer trends. This constant feedback loop ensures the models evolve with changing fraud tactics, shifting consumer behaviors and seasonal transaction patterns.

Looking ahead, Stratman said he sees a future where AI is not just an enhancement, but the foundation of the payments ecosystem.

In the world of payments, where milliseconds matter and fraud costs scale exponentially, “better results” thanks to AI aren’t just a competitive advantage, he said. They’re the difference between leading the market or chasing it.

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