The Form PF Delay: What Lies Beneath the Iceberg
The amended Form PF regime has been pushed out to October 1, 2026, after two earlier extensions. As the previous deadline approached, firms and industry bodies like the Managed Funds Association raised red flags about readiness.i Technical details like final filing specifications and clarity on requirements remain unsettled. Some firms are slowing programs to see whether the rule may be softened.
Hesitating now overlooks a critical trend. Regulators are looking for more granular reporting. The shift from what regulators “used to get” to what they “now need” represents a wide gap, driven mainly by the level of disaggregation required. And it isn’t limited to Form PF. Similar patterns are emerging in derivatives and futures reporting, as well as in other jurisdictions, including the European Union, Singapore, and the United Kingdom.
These requirements link multiple parts of an organization and force data to flow across security reference datasets, P&L statements, currencies, risk systems, and fund administrator feeds. The underlying systems were never designed to communicate with one another, so each compliance data point rests on a broad, interdependent foundation. The real work sits beneath the waterline, aligning instruments, entities, and exposures in a way that can stand up to monthly reporting expectations.
System fragmentation and the challenge of alignment
If you look under any single Form PF question, you quickly see how many systems it is actually pulling from. It becomes a questionnaire where each item links different parts of the organization together. You have security reference data, P&L, currencies, and risk systems layered on top of trade and position data. Then there is a separate fund and investor allocations platform, and often a fund administrator in the mix, with its own view of the same positions and entities.
Calculating exposure is an excellent example. The number a fund appears to have will be different if you pull it from the accounting system versus the risk system. Risk uses its own templates, its own pricing sources, its own way of looking at the world, so those numbers will never match by default. Each system has its own identifiers: The same stock might be “APPL” in one place and “Apple Inc.” somewhere else. When risk and accounting sit on different vendor platforms, matching records becomes a nightmare.
Cleaning this up is, in theory, a one-time exercise, but it is a huge one. You have to go through and agree where A in one system is the same as B in another, across all of the major datasets. That identifying process has never really been part of normal day-to-day operations; it only exists because Form PF is forcing it. Even after that, the mappings are fragile. If a vendor decides to change B to B-prime, the people running system A may not even know until much later, when they discover some positions were never matched correctly, and the reporting was off.
The structural work and mapping have to be in place before you can even start dealing with the new demands around how positions are classified, disaggregated, and traced back to details such as their ultimate issuers, countries, and execution venues.
Granularity, disaggregation, and data that doesn’t exist yet
Once you’ve connected the systems well enough to answer a basic Form PF question, the next problem shows up: The form isn’t just asking for the same old numbers. It is asking for new levels of granularity, and in some cases, data that has never been produced before. The easiest way to see that is to walk through a few concrete examples.
When you stand back from these examples, it becomes clear that no single system or point solution can solve the problem on its own. The only sustainable way to handle this kind of detail is to rethink the underlying data architecture and centralize the compliance view of the iceberg in one place.
From open waters to compliance data lakes
Once you get beneath the surface of Form PF, you see how quickly the realities of your data make it seem like you’re in open waters. Each new question pulls from separate systems for risk, accounting, pricing, fund administration, and allocations, and is often separated out by asset class, as well.
That pattern creates inconsistencies because each system uses different templates, identifiers, and pricing sources. When the underlying data cannot be reconciled through a single path, it becomes almost impossible to explain differences across reports or time. It creates very real exam risk.
The goal now is to move toward a compliance data lake that gives managers a single way to understand and control the whole iceberg. Pulling data from all upstream systems into a shared foundation makes it possible to standardize identifiers, hierarchies, and exposure rules so they behave consistently across reporting regimes.
A true compliance lake becomes a hub that holds the whole data iceberg in one manageable environment with governance. That includes:
- A single source of truth to support audit trails and regulatory exams
- Clear lineage across trades, positions, classifications, and derived exposures
- A scalable structure that can absorb new jurisdictions pushing toward PF-style reporting
With that infrastructure in place, firms can shift from reactive fire drills to a more stable operating model that can handle Form PF as well as what comes next.
Readiness, timelines, and building a future-proof foundation
Getting from scattered systems to a usable compliance lake is not something firms can compress into a quarter or two. The underlying systems will never fully reconcile on their own because each is built with different identifiers, pricing sources, and templates. You can’t solve that at the system level. The only way forward is to pull the data out and reconcile it in one governed layer.
In practice, that effort takes multiple developers working in parallel and a long cycle of refining the rules, reconciling the edge cases, and testing at full data volume. Even with a dedicated team, it can take anywhere from 12 to 18 months of calendar time to establish the core infrastructure and make it stable enough for downstream reporting.
Once the mechanics are understood, the next question is readiness: where the data is coming from, who owns it, what’s missing, and whether it is calculated at the required granularity. Some firms have never produced risk at the feeder level or disaggregated entities beyond the economic-unit view, so they need to rebuild those calculations from scratch. Operations teams must review monthly outputs far more detailed than anything they have seen before.
A future-ready foundation comes from decoupling the data from the filing format, with parameterized rules that adapt as questions evolve. The same governed lake then supports Form PF, as well as equivalent regimes emerging in other jurisdictions or asset types, giving firms a single, scalable, and compliance-ready infrastructure that can absorb the next round of changes.
Authored By
Layesh Pallath
Layesh has more than two decades in the financial services industry, and brings a blend of expertise from business operations, technology, and consulting. His current focus is to craft effective and scalable solutions to address regulatory compliance for the financial services industry.
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[i] Managed Funds Association, 2025. https://www.mfaalts.org/wp-content/uploads/2025/09/MFA-Letter-to-SEC-and-CFTC-re.-Form-PF-Extension-Request-As-submitted-on-9.9.25.pdf
[ii] SEC, 2024. https://www.govinfo.gov/content/pkg/FR-2024-03-12/pdf/2024-03473.pdf
