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Enterprise investments in data storage, infrastructure and pipelines are increasingly serving as the foundation for activating streams of operational and payments information. In this environment, the competitive advantage is shifting from simply accumulating data to using it with intent to drive faster, more accurate decision-making, Maverick Payments vice president of product Justin Downey said in a discussion for the April edition of the “What’s Next in Payments” series, “The Data Game.”
Downey said the distinction between data-rich and data-effective organizations is becoming “intent.” Data is no longer a differentiator on its own; it is the raw material for differentiation. While many businesses still store information in sprawling systems, leaders are focusing on how data feeds continuous decision-making loops.
What is changing, Downey said, is not reliance on data but the scale and speed at which it can be operationalized. He noted that AI is compressing the time it takes to reach decisions, rather than replacing human judgment—an approach that can be especially transformative in payments, underwriting and fraud detection where speed and accuracy are closely linked.
For Maverick, Downey said much of the transformation is concentrated at the start of the customer lifecycle, where underwriting—often viewed as a compliance-heavy bottleneck—is being reimagined as a strategic lever for both growth and trust.
“The biggest gains for us happen before the first transaction even occurs,” Downey said, describing a process aimed at removing redundant questions and moving good clients through onboarding more quickly. He added that when friction appears, the goal is to identify where it occurs and make it “smarter friction.”
The approach emphasizes eliminating unnecessary manual steps while preserving rigor. Downey cited identity verification as a way to replace redundant data entry, while intelligent escalation routes edge cases to human experts. The objective is a system that is both faster and more discerning, delivering a seamless, positive customer experience.
Downey said the strategy reflects a broader shift in digital experience design: instead of applying uniform checks across all users, companies are increasingly segmenting interactions based on risk signals. Low-risk users move through onboarding with minimal disruption, while suspicious activity triggers deeper scrutiny.
He also warned that fraudsters are adopting automation and AI, meaning defensive systems must keep pace. “If you’re a company that isn’t using elements of AI, you’re going to fall behind,” Downey said.
Despite rapid AI adoption across enterprises, Downey emphasized that maturity is not defined by usage alone, but by governance. He said the next phase of the AI cycle will be shaped by how well companies establish guardrails to ensure tools are deployed responsibly and effectively.
Downey stressed that AI should not be treated as complete automation. Instead, he described a workflow where AI is turned on to route data to human experts and is built into day-to-day processes. By surfacing patterns and outliers quickly, AI helps decision-makers focus on higher-order questions such as why something is happening, where risk is concentrated, and how strategy should shift.
Downey said the governance and workflow approach is particularly relevant in heavily regulated industries, where compliance complexity can slow innovation. He added that embedding data into long-term innovation roadmaps can help organizations anticipate regulatory shifts while continuing to scale.
“Your roadmaps are a year, two years, three years out,” Downey said, adding that the data can help inform the target organizations are trying to hit in the distance.
In that sense, Downey said the competitive moat around data is increasingly built not on accumulation, but on intent.
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