The Fed Wants to Reduce Regulatory Burden. Your Data Infrastructure Might Not Be Ready for What That Actually Means.
A bank's regulatory reporting team might spend the better part of two weeks each quarter manually reconciling general ledger extracts against call report line items. They pull data from three systems, normalize it in spreadsheets, flag discrepancies row by row, and document every adjustment for the examiner file. When the Federal Reserve announces it wants to "reduce regulatory burden," this team doesn't celebrate. They brace. Because in their experience, every simplification on paper creates a new layer of complexity in practice.
That instinct is well-calibrated.
The Federal Reserve's December 2025 Supervision and Regulation Report outlined several regulatory developments, including updates to supervisory approaches and the ongoing Economic Growth and Regulatory Paperwork Reduction Act (EGRPRA) review process. The stated goal is straightforward: identify outdated or unnecessarily burdensome regulations and streamline them. On the surface, this is welcome news for institutions that have spent the last decade layering compliance processes on top of each other. But the operational implications are more nuanced than any press summary suggests.
Reducing regulatory burden does not mean reducing data expectations. If anything, the trajectory is the opposite. Supervisors are signaling that they want fewer redundant reports — but higher confidence in the data that remains. The emphasis is shifting from volume of compliance artifacts to quality of underlying data controls. And that shift lands squarely on the teams responsible for data governance, reconciliation, and audit traceability.
Fewer Reports, Higher Stakes Per Report
The EGRPRA review, mandated by Congress every ten years, requires federal banking agencies to examine whether existing regulations are outdated or impose unnecessary burden. The Federal Reserve has been soliciting public comment and reviewing reporting requirements across multiple categories. Some of this work has already resulted in proposed changes to call report schedules and reduced frequency for certain filings.
This sounds like relief. For some institutions, it will be.
But consider what happens when you reduce the number of reports an institution files. Each remaining report carries more weight. Examiners have fewer data points to cross-reference, which means the data points they do receive get scrutinized more carefully. A discrepancy that might have been caught and contextualized across three overlapping filings now stands alone. The margin for error shrinks.
For the teams producing those reports, this changes the calculus. You can't rely on downstream corrections or examiner conversations to resolve ambiguities. The data has to be right at the point of submission. And "right" doesn't just mean accurate — it means traceable. Every figure needs a clear lineage back to source systems, with documented validation steps and exception handling along the way.
This is where the gap between regulatory intent and operational reality becomes visible. The Fed is trying to reduce paperwork. But the infrastructure required to produce fewer, higher-quality reports is often more demanding than what was needed for the old volume-based approach. You're not eliminating work — you're concentrating it.
Imagine a mid-size bank filing quarterly call reports that currently reconciles tens of thousands of rows across multiple GL extracts, flags hundreds of exceptions, and resolves them through a combination of manual review and tribal knowledge. If several of their supplemental filings are retired, the remaining filings don't suddenly become easier. The same underlying data complexity exists. The same source system inconsistencies persist. The same mapping tables need maintenance. What changes is that the institution now has fewer opportunities to demonstrate competence and more pressure on each demonstration.
The Data Governance Layer That Regulation Assumes but Doesn't Build
One of the more revealing aspects of the Fed's supervisory updates is the increasing emphasis on operational controls and data quality — not as a compliance checkbox, but as a supervisory expectation embedded in examination procedures. The December 2025 report references enhancements to supervisory approaches that include closer attention to how institutions manage operational risk, including technology and data risk.
This is not new in concept. Examiners have always cared about data integrity. But the formalization of these expectations — and their integration into broader supervisory frameworks — creates a practical problem for institutions that have historically treated data governance as a downstream function of IT or finance, rather than a first-class operational discipline.
Consider the typical architecture. A bank's core banking system generates transaction data. That data flows into a general ledger. The GL feeds reporting systems. Reporting teams extract data, transform it, and submit it to regulators. At each handoff, there's a potential for drift: rounding differences, timing mismatches, mapping errors, schema changes that weren't communicated. The reconciliation process exists to catch these issues. But in many institutions, reconciliation is still a manual, periodic activity — not a continuous, automated control.
A major regulatory fine for data control failures, widely reported in the compliance press, underscored what happens when data controls fail at scale. While such cases often center on communication surveillance rather than financial reporting, the principle is identical: if you can't prove completeness and accuracy of your data, the regulatory consequences are severe. Custodia Technology argues that reconciliation is effectively no longer optional in regulatory communication data compliance and is key to proving data integrity.
The Fed's current direction amplifies this dynamic. As supervisory expectations for data quality become more explicit, institutions need infrastructure that doesn't just produce correct outputs but can demonstrate how those outputs were produced. Audit traceability isn't a nice-to-have — it's the thing that separates a clean exam from an MRA.
And yet, the tools available to most financial data teams were not designed for this. They were designed for matching transactions, not for proving that every step in a multi-system data pipeline maintained integrity. They handle the reconciliation — the row-by-row comparison — but not the governance wrapper around it: the lineage, the exception documentation, the version control, the change tracking that examiners increasingly expect to see.
This is the infrastructure gap that regulatory simplification, paradoxically, makes worse. When you had ten reports, you could afford some looseness in your controls because the redundancy provided a safety net. When you have five reports, each one has to be airtight. And "airtight" requires infrastructure that most institutions haven't built yet.
What the EGRPRA Cycle Reveals About Operational Debt
The EGRPRA review process itself is instructive. Every ten years, the banking agencies ask: which regulations are outdated? Which impose unnecessary burden? The answers are predictable — institutions cite reporting requirements that overlap, data fields that are no longer relevant, submission frequencies that don't match business cycles.
But the deeper pattern is more interesting. The regulations that institutions find most burdensome are often the ones that expose the most operational debt. A reporting requirement isn't burdensome because it asks for data — it's burdensome because the institution's systems aren't designed to produce that data efficiently. The burden isn't in the regulation. It's in the infrastructure.
A fintech processing payment transactions across multiple networks faces this acutely. As Osfin.ai has documented, the reconciliation process for fintechs — ingesting data from disparate sources, matching transactions across internal ledgers and external networks, handling exceptions, and generating audit-ready reports — is fundamentally an infrastructure challenge. The regulatory requirement to demonstrate accuracy is just the forcing function that reveals whether that infrastructure exists.
The same is true for banks navigating the EGRPRA cycle. When an institution says a reporting requirement is burdensome, what they're often really saying is: "Our data pipeline can't produce this output without significant manual intervention." Eliminating the requirement removes the symptom. It doesn't fix the pipeline.
This matters because the Fed's broader supervisory direction — enhanced attention to operational risk, data quality, and technology controls — means that even if specific reporting requirements are retired, the underlying expectation for sound data infrastructure remains. You might file fewer reports, but you still need the same (or better) data controls. The examiner who used to check your call report accuracy will now check your data governance framework directly.
FinTech Global reported on the hidden costs of poor compliance reconciliation, noting that capturing data alone fails to provide defensible proof of completeness. The insight applies broadly: institutions that have invested in data capture without investing in data validation and integrity verification are exposed. The regulatory environment is moving toward a model where the controls matter as much as the outputs.
For financial data teams, this is both a challenge and a clarification. The challenge is obvious — building or upgrading infrastructure to meet higher data quality expectations is expensive and complex. The clarification is that the direction of travel is no longer ambiguous. Whether through EGRPRA streamlining, enhanced supervisory procedures, or enforcement actions, the message is consistent: data integrity is a supervisory priority, and the infrastructure to support it is not optional.
SafeBooks.ai describes how AI-driven anomaly detection and automated pattern recognition can streamline reconciliation and data validation, offering part of the answer. But technology alone doesn't solve the problem. The gap is also architectural: how data flows between systems, how exceptions are documented and resolved, how lineage is maintained across transformations, and how all of this is made visible to examiners without requiring a two-week manual exercise.
These are infrastructure problems. They sit at the intersection of finance, operations, and technology. They aren't solved by adding another compliance layer on top of existing systems. They're solved by rethinking how financial data is validated, compared, and traced from source to submission.
The Fed's regulatory developments don't create these problems. They reveal them. And for teams that have been managing data integrity through spreadsheets, manual reconciliation, and institutional memory, the window for that approach is narrowing.
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