Mastercard Sees Data Moving Payments From KYC to KYA

Watch more: What’s Next in Payments With Mastercard’s Kaushik Gopal

Both risk and opportunity across the payments industry are being increasingly defined by what happens in a millisecond—and what doesn’t.

“What AI (artificial intelligence) has done is enable us, in that real time, in that moment, to process multiple input signals and create a composite view of whatever decision we’re making,” Kaushik Gopal, executive vice president, insights and intelligence at Mastercard, told PYMNTS during a discussion for the April edition of the “What’s Next in Payments” series, “The Data Game.”

“Data isn’t a game,” Gopal said. “It’s foundational to our entire business.”

For a network like Mastercard handling billions of transactions, even marginal improvements in fraud detection, conversion or customer experience can translate into outsized impact. But those gains depend on data being both usable and responsibly governed.

Gopal described a “flywheel” model in which transactions generate data, data produces insights, and insights feed back into better decisions across the ecosystem. The catch is that this loop only works if participants believe in it.

“It’s all centered around one word, and that’s trust,” Gopal said. “Making sure that people in the ecosystem — consumers, merchants included — understand that we manage data based on principles of trust, privacy and transparency.”

Why Payments Success Hinges on Speed, Signals and Trust

If trust is the foundation for data to have its greatest effect, artificial intelligence (AI) is becoming the engine. The shift in terms of real-world impact is less about novelty than it is about velocity.

“AI didn’t just arrive overnight—we’ve been using it for years,” Gopal said. “It’s a computational tool that allows us to accelerate the outcomes of converting data into useful and actionable insights.”

For example, while early fraud systems relied on static rules like thresholds that triggered approvals or declines, today, Mastercard processes multiple signals simultaneously, from behavioral patterns to geolocation, in real time. The decision window has shrunk to milliseconds.

This evolution from rules-based systems to time-series models, graph analytics and now AI has transformed the economics of decision-making. It’s no longer just about accuracy; it’s about balancing risk with experience. A false decline can be as costly as fraud itself, especially in eCommerce environments where friction leads to abandonment.

“Every interaction and transaction has to be viewed in its own context,” Gopal said, adding that this context spans three temporal layers: before, during and after the transaction.

What was once simple, like flagging a jewelry purchase in Thailand for a customer who never traveled, is now far more complex. Cross-border eCommerce has blurred geographic signals, requiring systems to interpret a richer set of variables, from IP addresses to merchant location and transaction currency.

In response, identity verification, behavioral analysis and post-event feedback loops (such as chargebacks) now can all contribute to a continuously evolving risk profile evaluated not in isolation but as part of a broader behavioral narrative.

“It creates a virtuous cycle in terms of how we manage fraud risk in the AI age,” Gopal said. “Converting data into insights gives us the intelligence to get ahead of bad actors.”

Winning With Data Over the Next Phase

While fraud prevention remains the most immediate use case of data application, Gopal also sees broader implications for customer experience. Data, in this view, becomes integral not just for dashboards or reports, but to power interactive, adaptive intelligence.

Instead of requiring expertise in analytics platforms, users ranging from bank executives to retail operators may increasingly rely on AI-driven interfaces that interpret data and recommend actions.

“The way our customers interact with our tools is going to fundamentally shift,” Gopal said. “You go from self-discovery to chat-assisted discovery to agentic support.”

Another consequential application of data that’s gaining momentum today lies in credit underwriting, particularly for “thin file” consumers and small businesses that lack traditional credit histories.

“The more data sets that you have and the better modeling techniques, the more you can start to support the areas that are underserved,” Gopal said.

By combining payment data, cash flow signals and open finance inputs, Mastercard aims to provide a more holistic view of financial health. The same capabilities that detect fraud such as pattern recognition, real-time analysis and multi-signal modeling can increasingly be leveraged to expand access to capital.

Enter the Agentic Economy

For payments networks, the emerging phase of agentic commerce and payments may mean rethinking everything from authentication protocols to fraud models, as transactions are no longer initiated directly by humans but by autonomous software acting on their behalf. The agentic era could also open new opportunities in areas like automated discovery, where agents negotiate offers on behalf of users in real time.

“You were identifying a consumer. Now you’re identifying an agent. New fields and new data elements are going to emerge in the transaction that didn’t exist before,” Gopal said.

This shift is already introducing new layers of complexity and new definitions of trust as identity extends to include digital agents, requiring a transition from know your customer (KYC) to what Gopal called “know your agent” (KYA).

As AI capabilities accelerate, the question for enterprises is not whether to adopt them, but how. Mastercard’s own advisory work with clients reveals a recurring challenge: prioritization. Attempting to do everything at once can often lead to failure. Instead, companies can work to better align data strategy, infrastructure and use cases with clear business objectives.

“Ensuring that you know what outcomes you want is really, really key,” Gopal said. “And having an execution partner to help you get you there is equally important.”

Equally important is discipline. “AI is only as good as the data that you have and how structured that data is,” he said, stressing that testing environments, sandboxing and incremental deployment remain essential even as competitive pressure pushes organizations to move faster.

Ultimately, the future of payments may hinge less on any single technology than on the interplay between trust, data and intelligence. Mastercard’s bet is that these elements will reinforce one another: Data fuels insight, insight improves outcomes and better outcomes strengthen trust.