Obligor-Level Data:
The Gap Behind Private Credit's Governance Headlines

Key Takeaways

• The collapse of specialist lender Market Financial Solutions (MFS) in February 2026 put a specific failure mode on the industry's radar: fragmented data across managers, servicers, trustees, and financing vehicles that makes double-pledging and misreporting hard to catch.
• Regulators responded with disclosure-focused reports. None of the three major ones, from the Fed, the FSB, and the ECB, actually diagnosed the operational root cause.
• The root cause is an infrastructure gap. Most firms cannot see the same obligor's exposure consistently across every facility, servicer feed, and financing vehicle it touches.
• Obligor data management and financial spreading are the disciplines that close that gap, turning fragmented borrower documentation into a single, verifiable, source-linked record. Combined with enterprise data management, they create a reliable foundation for investment portfolio monitoring and risk oversight.

Private credit has spent the past decade being told largely through one story: direct lending. Corporate loans, sponsor-backed deals, unitranche structures. That story is still true, but it is no longer the whole picture.

Oxane's Compass 2026 survey, a credit-only study of more than 380 senior professionals across North America and Europe, found that asset-based finance and specialty finance has overtaken direct lending as the most widely held strategy in private credit. Sixty-six percent of firms surveyed are active in ABF today. Fifty-six percent are active in direct lending. Fund finance sits in between at 60%.

Every private credit governance conversation in 2026 eventually arrives at the same word: fragmentation. Not fraud specifically, not any single lender's failure, but the underlying condition that makes fraud and misreporting hard to catch in the first place. Data about the same borrower sits in different formats, held by different counterparties, updated on different schedules, with no single place that ties it all together.

The result is a lack of consistent obligor level data, making it difficult to verify exposures, identify overlaps, and assess risk across counterparties in real time.

The collapse of Market Financial Solutions (MFS), a UK specialist bridging lender, in February 2026 turned that abstraction into a live case study. MFS's owner categorically denies the fraud and double-pledging allegations that followed, and the investigation into what actually happened is still ongoing. But the market reaction to it revealed something worth examining regardless of how that investigation concludes: how exposed the private credit ecosystem still is to a single borrower's data being inconsistent across the parties financing it.

What the MFS Situation Exposed

Kanav Kalia, Managing Director at Oxane Partners, was quoted by GlobalCapital shortly after the collapse, describing how banks were expected to respond. Everyone is very cautious now about being exposed to fraud or misreporting, he said, and clients are pushing for a genuinely deep dive on transaction data rather than the periodic checks that had been standard practice.

Sumit Gupta, CEO and Co-Founder of Oxane Partners, made a related point to CNBC when the MFS story broke more broadly into the mainstream press. Complex funding chains need equally robust operating controls, he said, framing the situation less as a referendum on private credit's credit quality and more as a test of whether firms can actually see their own risk. It exposes how hard it can be to see risk clearly when data is fragmented across managers.

Both quotes point at the same gap from different angles. One is about verification before a facility is drawn. The other is about visibility once it is live. Neither is solvable through better disclosure alone, because disclosure assumes the underlying data is already reconcilable across parties. In cases like MFS, it often is not.

Why the Regulatory Response Missed the Operational Root Cause

In the months following MFS, the Financial Stability Board, the European Central Bank, and the US Federal Reserve each published reports assessing risk in private credit. Private Debt Investor's coverage of the three reports, in its July/August 2026 issue, drew a sharp distinction between them. The Fed's report focused narrowly on liquidity mismatches in semi-liquid fund structures and reached a bounded, defensible conclusion. The FSB's report drifted from a narrow definition into a broad one, lumping private equity-owned insurers, significant risk transfer, and general corporate credit under a single heading, which produced unease rather than a specific, answerable question.

The ECB's report came in for the sharpest criticism. Alternative Investment Management Association's Alternative Credit Council characterized its approach as building a market-wide crash scenario into the assumptions rather than deriving it from the data. Having found little direct risk, the ECB turns to a simulated shock, as the analysis put it, layering a severe software-borrower stress, a private credit default wave, and a 30% equity market fall on top of a segment the report itself concedes is small and largely offshore.

What all three reports share, regardless of how rigorous each is individually, is a focus on disclosure obligations and systemic exposure thresholds. None of them addresses the more mundane, more fixable problem sitting underneath the MFS situation: whether a single obligor's exposure can actually be reconciled across every manager, servicer, trustee, and financing vehicle touching that borrower at any given moment. That is not a disclosure gap. It is a data infrastructure gap, and it exists well below the level any of these three reports were built to examine.

What Obligor-Level Data Management Actually Means

Obligor data management is the practice of tracking a borrower's full exposure, collateral, and reporting history as a single, continuously reconciled record, regardless of how many facilities, servicers, or financing vehicles that borrower touches. In asset-based and specialty finance, where the same underlying assets or receivables can theoretically be pledged across more than one facility if oversight is weak, this is the layer that would need to catch a double-pledging pattern before it becomes a five-alarm story.

Put simply, for anyone asking what is obligor level data, it is a consolidated view of a borrower's exposure, collateral, performance history, and reporting information across every facility and financing structure linked to that borrower.

In practice, this means tying every document, loan tape entry, and servicer update back to a specific obligor identity, then cross-referencing that identity's exposure across every facility it appears in. Done manually, this is exactly the kind of work that falls through the cracks when volumes rise and reporting cadences differ by counterparty, which is precisely the condition most asset-based finance portfolios operate under today.

This also enables obligor level performance tracking, helping lenders monitor changes in borrower health, collateral quality, and exposure concentrations over time.

Financial Spreading: The Layer Beneath Obligor Tracking

Financial spreading, the process of extracting figures from a borrower's financial statements and standardizing them into a consistent, comparable format, is the discipline that makes obligor-level tracking possible at scale. Borrower financials arrive as PDFs, scanned documents, and inconsistent templates. Spreading converts that into structured, comparable data tied back to the source document it came from.

Without accurate spreading, obligor-level views are only as good as whatever was typed into a spreadsheet by hand, with no reliable link back to the original filing if a number needs to be checked later. With it, every figure in an obligor's record can be traced to its source, which is the exact audit trail that becomes valuable the moment a counterparty asks hard questions about a specific borrower.

How Enterprise Data Management Closes the Gap

This is precisely the layer Oxane Panorama's Enterprise Data Management module is built to address. Effective enterprise data management allows firms to reconcile borrower information coming from multiple documents, counterparties, and servicing channels into a single trusted view.

Unstructured documents, loan tapes, and servicer reports are ingested as they arrive, in whatever format they come in, rather than forcing counterparties to standardize before submission. AI-driven extraction then pulls the relevant fields, and multi-level validation checks those fields against historical patterns, covenant thresholds, and cross-file reconciliation before anything is treated as reliable.

The result is a single source of truth for each obligor, with every data point traceable back to the document it came from and tracked against every counterparty responsible for delivering it. That traceability is what turns a periodic spot-check into a continuous verification process, and it is the specific capability the MFS situation showed the market it was missing.

What This Means for Banks and Funds Right Now

Oxane's own Compass 2026 survey, fielded across more than 380 senior credit professionals in the months immediately following the MFS situation, found that 42% of firms now cite risk management and valuation confidence as their top operational challenge, the highest-ranked concern in the entire dataset. That figure is not a coincidence.

It reflects growing demand for stronger portfolio risk analytics and more reliable investment portfolio monitoring capabilities across increasingly complex credit portfolios. It reflects an industry that has just watched, in real time, how quickly fragmented data can turn into a governance headline.

The same survey found that banks are re-tranching rather than retrenching, increasingly funding senior layers of deals while private credit managers take on more asset-linked exposure. That structural shift makes obligor-level visibility a shared problem rather than a single firm's internal concern. When a bank and a fund are financing different layers of the same borrower's exposure, both parties need a consistent, reconciled view of that obligor to trust the arrangement at all.