When customers open their banking app on a Sunday and see a payment still pending, they don’t think about settlement timing or payment infrastructure. They ask a simpler question: Why isn’t my money there yet? That question is not a feature request. It’s a signal.
Elena Casal, Chief Client Officer at The Clearing House, has spent years watching financial institutions misread it.
“Customers might not be asking their FI for real-time payments by name,” she said. “They’re asking why their money isn’t available yet… That’s the demand for real-time payments. It just doesn’t look like a specific product request.”
What customers experience as delay, institutions often fail to recognize as demand. That gap between expectation and prioritization runs through nearly every consequential challenge in financial services today.
In the U.S., that demand is accelerating. The Clearing House reports that the RTP network processed 125 million transactions totaling $405 billion in Q4 2025 alone, bringing total volume past $1 trillion since launch. Meanwhile, the Federal Reserve’s FedNow service continues expanding participation across banks and credit unions, reinforcing the shift toward always-on payments.
As Women’s History Month prompts familiar conversations about representation in fintech, a more instructive story is emerging. A group of women leaders are building the payments rails, data systems, compliance frameworks and partnership models that determine whether the industry’s ambitions translate into operational reality.
These are the roles where the real decisions get made—where governance frameworks are defined, partnership risk is taken on, and the standards others will eventually follow are set.
When Data Becomes the Prerequisite
Before a bank can deploy AI effectively, it must first build data it can trust. That requirement sounds straightforward. In practice, it is where most institutions fall short.
Most aren’t limited by access to data or investment in technology. The constraint is whether they’ve built the discipline to use it consistently and at scale.
Shanthi Pudota, Senior Vice President and Chief Data and Analytics Officer at BOK Financial, is focused on building that discipline. Her definition is more operational than most technology narratives.
“Operationally, a trusted data foundation means a bank has put the right strategy, capabilities, and behaviors in place so data can be used confidently, repeatedly, and responsibly at scale,” she explained. Before AI can do anything useful, she added, a bank needs far more than technology. It needs operational clarity and discipline.
That distinction shows up in how data is managed. It requires clear ownership, consistent definitions and full visibility into how data moves across the organization. “Lineage isn’t documentation for documentation’s sake,” Pudota said. “It’s what enables trust, change management, regulatory confidence, and responsible AI.”
At BOK Financial, that approach is embedded into how work gets done rather than treated as a checkpoint. The goal is not to control data after the fact, but to make it usable and reliable from the start. The difference is most visible in initiatives like Customer 360, which aim to create a single, unified view of the customer across systems.
“It’s often described as a technology problem,” Pudota said, “but in banking it’s fundamentally a governance challenge.” The difficulty lies in aligning definitions, ownership and accountability across the organization.
Why Data Strategy Still Breaks Down
The problem is not a lack of data, but how institutions structure decisions around it.
Sarah Biller, Co-founder of Fintech Sandbox, sees this most clearly in how organizations approach data activation. “One significant mistake that startups and smaller FIs make is treating data activation as a tooling exercise rather than a business discipline,” she said.
In practice, that means institutions continue to invest in platforms and vendors without resolving more basic questions—who owns the data, how it is defined, and how it is governed across the organization. That misalignment becomes even more visible at the community bank level.
Julieann Thurlow, President and CEO of Reading Cooperative Bank and Co-chair of the Massachusetts Fintech Hub, operates at the intersection of ambition and constraint.
“There is a natural tension between the pace of fintech innovation and the deliberate, risk-aware culture of community banking,” Thurlow shared.
At times, she champions ideas at the fintech hub that her own institution is not yet positioned to adopt. “That is okay,” she added. “It reflects the different perspectives that banks and fintechs bring to innovation.” Her role highlights a practical reality often overlooked in broader fintech conversations: innovation only works when the institution is prepared to support it.
For Reading Cooperative Bank, the focus is on building the technological and cultural groundwork that makes future adoption possible. Thurlow describes it as getting “the horse and the cart in the right order.” Across institutions, that level of sequencing is still the exception.
The Partnership Model’s Accountability Gap
The consequences of weak operational foundations become most visible in sponsor banking. Bank-fintech partnerships tend to work well—until they are tested. Regulators have continued issuing consent orders tied to fintech partnership oversight, keeping these models under sustained scrutiny. In many cases, institutions simply scaled faster than their oversight capabilities could support.
Amanda Swoverland, President of Hatch Bank, has spent more than fifteen years working at the intersection of banking and fintech. “Banks underestimate how critical strategy discipline is in this space,” she said. “Partner selection shouldn’t start with who is exciting or fast-growing. It should start with a clear view of what the bank is actually built to support.”
“Understanding the operational, customer, and regulatory impact of failure early can be the difference between a manageable issue and a crisis,” she added. For fintechs, that makes the sponsor bank relationship foundational, not interchangeable. “If a fintech treats its sponsor bank as a commodity, it never builds the relationship equity needed to navigate those moments,” Swoverland said.
Biller sees the same structural weakness across the ecosystem. “The model is most fragile where accountability is diffused,” she said. “We operate in an ecosystem where fintechs, sponsor banks, and middleware providers each own a piece of the stack, but not always the outcome.” When everything works, that fragmentation looks like efficiency. Under stress, it reveals itself as risk—and makes clear who is ultimately accountable.
Compliance Is a Design Decision
Regulatory strategy is where many fintech companies delay decisions the longest—and where those decisions are hardest to reverse. The instinct to move quickly and address compliance later can accelerate early growth. It also creates structural risk that becomes difficult and expensive to unwind.
Emily Goodman, Partner at FS Vector, has seen how often companies defer these decisions until urgency forces action. “One of the most common general mistakes is treating compliance as something you can layer in or build later,” she said. In practice, that approach often leads to rework—systems redesigned, products restructured and timelines extended.
“Your compliance advisors… should be involved from day one… This can help avoid costly re-architecture later.” The consequences become more visible as companies scale. “At a certain point, often driven by growth, investor expectations, or a strategic transaction, licensing becomes urgent,” Goodman said. “Licensing is not something you can turn on overnight. It requires extensive planning, infrastructure, and capital.”
By the time that urgency sets in, the available options are often narrower and more constrained than they would have been earlier. The pattern is consistent. Compliance decisions made late reshape how companies grow.
The Infrastructure Is Ready. Are The Banks?
In real-time payments, the technology problem has largely been solved. What remains is an institutional readiness gap. The infrastructure exists. The question is whether banks are prepared to operate at its speed.
That gap is not new. It reflects the same pattern seen across data, partnerships and compliance: capabilities advancing faster than the structures required to support them.
“The barrier is more often a mismatch between how demand actually shows up and how banks interpret it,” Casal said. For institutions that have adopted RTP, the benefits extend beyond customer experience. “FIs that have adopted real-time payments aren’t just offering a new feature,” she noted. “They are gaining operational efficiencies.”
But speed introduces a different kind of pressure, particularly around fraud and control. “For institutions that only receive RTP payments, the control changes are generally less significant than for institutions that enable send,” she said. That distinction matters. A credit-push system shifts responsibility to the sender side, requiring controls that operate at the same speed as the network itself.
“Speed without stability can hinder progress,” Casal said. “The banks that understand that aren’t moving slower. They’re treating fraud controls as a precondition for scale rather than something to address once volume arrives.”
Across each of these areas, the same lesson emerges. The work that looks like infrastructure is what ultimately determines what scales and what fails.
Real-time payments, data foundations, partnership models and regulatory strategies are often treated as separate challenges. In practice, they are the same problem viewed from different angles—aligning ambition with the operational discipline required to support it. The women leading these efforts are not working at the edges of fintech. They are shaping the systems underneath it, defining how decisions are made, how risk is managed and how innovation is sustained.
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