Green Dot Says the New Data Moat Is Speed

Watch more: What’s Next in Payments With Green Dot’s Akhil Gupta

Enterprise C-suites are swimming in a sea of data. Collecting information, after all, is the easy part. It’s connecting that data across its disparate sources, then acting on it, that is now proving to be a key operational challenge, especially across finance departments.

But that ability of a business to act on its data is also emerging as a key competitive differentiator.

“The game is changing. Data itself is no longer the moat, it’s the speed and confidence with which companies can use that data to turn it into decisions at scale,” Green Dot VP of Product Akhil Gupta told PYMNTS during a discussion for the April edition of the “What’s Next in Payments” series, “The Data Game.”

“The key difference for me in companies that are simply collecting data versus using that as a competitive moat is intentionality and actionability,” he said.

At the heart of this systems-level transformation of enterprise data are three interlocking capabilities.

First is integration: the ability to consolidate fragmented data streams across areas like identity, payments, customer support and more into a cohesive model. Second is decisioning: determining when to rely on automated systems versus human judgment. And third is learning velocity: ensuring that systems improve continuously as more data flows through them.

“The winners won’t just be the businesses that are fastest in collecting the data. They’ll be the ones that can move fast and turn the data into decisions and outcomes,” Gupta said.

The Falling Cost of Data Management and the Rising Cost of Ignoring It

As storage, processing and tooling costs around data continue to fall, access to information is becoming ubiquitous. Just as declining storage costs in the late 20th century unlocked the personal computing revolution, today’s drop in the cost of inference is increasingly enabling a new generation of decision engines.

In payments, this manifests in a reconfiguration of workflows that were historically dependent on legacy methods.

“The number of these manual review cases is going down because we have cases that are now resolved upstream,” Gupta said, noting that when human intervention is still required, it is augmented: “The cases are better annotated. There’s more data, more information for the human to work with.”

The result is that decisions once constrained by operational expense or human bandwidth can now be made faster, more frequently and with greater precision.

But Gupta was careful to frame AI not as a replacement for human judgment, but as an amplifier.

“AI is not replacing judgment or decision making in the payment space,” he said. “It’s augmenting it, at a speed and at a scale that was previously untenable.”

And there are few places where this shift is more visible than in fraud prevention, a domain that has historically relied on blunt, rules-based systems. To keep out fraud, these rigid systems often imposed a trade-off, where tighter controls reduced fraud but also inadvertently increased friction for legitimate users.

“We are moving away from a one-size-fits-all approach to more tailored precision,” Gupta said, noting that uniform safeguards are giving way to context-aware systems capable of adjusting controls dynamically based on the user’s risk profile.

“If it’s a good customer, they never even see the approach,” he added. “But when you’re facing a bot, you’re able to catch those scenarios much more easily.”

Rather than making companies choose between preventing fraud and providing a delightful customer experience, building fraud controls atop a strong and effective data foundation can combine them into a single step where they represent two opposite sides of the same coin.

How Firms Are Turning Data Exhaust Into Product Decisioning Engines

The ability to synthesize disparate data sources is also unlocking new markets among populations historically underserved by traditional financial systems. With fresh and alternative data, institutions can get a clearer look at the risk profiles of these cohorts.

Gupta pointed to “thin file” users as a case in point. These customers have long been excluded from mainstream banking due to rigid, rules-based underwriting. but today’s modern and more adaptive systems can construct identity and risk profiles from a broader array of signals, enabling the right product-customer fit in ways that were previously unattainable at scale.

Even in the absence of conventional data, Gupta said, firms like Green Dot are able to “stitch digital identities together” in a way that drives “a pretty intelligent risk-weighted assessment.”

The result is not just improved inclusion, but ultimately a rethinking of how financial services are delivered, with data-smart companies increasingly able to deploy dynamic flows tailored to different customer segments in near real time.

Looking ahead, Gupta sees success hinging not on any single capability, be it collection, synthesis or action, but on the growing interplay between them. For platform companies, that can mean rapidly integrating new data sources and tools into decision systems. For business leaders, it may mean balancing cost control with customer trust. And for end users, it could ultimately help drive new, stickier experiences that feel intuitive and even invisible.