A deterministic engine that recomputes what borrowers report against the documents that govern them — and refuses to judge what it cannot verify.
Private credit has crossed ~$3T and is still climbing. As of mid-2026 it had posted a record 6.0%default rate (Fitch, April 2026) — a systemic asset class without the verification infrastructure public markets take for granted.
Private credit has reached ~$3Tand is still climbing. As of mid-2026 it had become a systemic asset class — without the verification infrastructure public markets take for granted. Yet it still runs on PDFs, spreadsheets, and trust.
In late 2025, that caught up with the market.
Collapsed into Chapter 11 with roughly $2.3B in factoring obligations that sat off the main balance sheet, now under a court-appointed examiner.
Collapsed in 2025 after DOJ charges that the same auto collateral was pledged to multiple lenders — roughly $2.2B pledged against $1.4B of real collateral.
Both were diligenced. Both reported clean.
The lesson the market drew: reported and verified are not the same thing.
From First Brands' public Chapter 11 filing alone, UMFR's engine certifies a debt floor of $8.4B and reconstructs a ceiling of $10.7B — the gap driven by ~$2.3B of factoring disclosed only in prose, invisible to a headline reader (+27.2%floor to ceiling, every figure cited to its source page). The court-appointed examiner's report (filed April 27, 2026) puts total liabilities at $11.6B— external corroboration of the scale and direction the engine surfaces from public documents alone — and names the enabling failure, in the examiner's words: lenders 'typically did not see the actual underlying invoices or verify the data.'
We do not claim to have predicted the fraud. We claim something more useful: the verification that would have surfaced it was mechanically possible, on documents lenders already had — and no one was positioned to run it.
Take what the borrower reported — financials, collateral schedules, covenant certificates — alongside the source documents behind them.
Independently re-derive the numbers with deterministic logic. No black box, no model guessing at runtime — the same inputs always produce the same result.
Surface what does not reconcile as exceptions for review, with every figure traced back to the exact line in the source. A reviewer confirms in seconds.
One engine, run deterministically on real public filings at zero compute cost. Each proof below is a shipped output, every figure traced to source. Every report is self-verifying: recompute its SHA-256 to confirm it is unaltered since generation.
Name a public issuer and watch the engine run — deterministically, on the public record, at zero compute cost.
Different issuers, different credit types, one engine — every figure traced to source.
Several entrants now stake out verification. Setpoint shipped PledgeCheck (October 2025) as an independent collateral-integrity standard; Cascade Debt offers independent loan-data infrastructure. The field is contested, not empty.
To our knowledge, as of July 2026, no incumbent offers definition-level covenant forensics for private credit — recomputing covenant EBITDA from the agreement's own add-back definitions, caps and pro-forma adjustments.
Two supports sit beneath that lead: UMFR recomputes against the source agreement, where a data-tape standard matches against the tape; and it runs as a third party, where the incumbent is an operator embedded in the deal. UMFR publicly commits to deterministic verification with no LLM in its output — a commitment the incumbents have not made.
Verification is the entry point, not the destination — each layer makes the next more valuable because verified data compounds.
Independently recompute what borrowers report against the documents that govern them.
A registry of verified instruments — a corporate-credit lien-leakage focus, with privacy-preserving collision checks.
Turn verified data into the regulatory and compliance reporting lenders and insurers must produce.
Independent credit assessment and indices built on verified data.
The deterministic fact layer any AI tool can call to ground an extracted figure.
A neutral venue where verified instruments can change hands.
Detectors get copied; institutions compound.
An open framework UMFR publishes and uses — and one the market may adopt — but not an industry-adopted standard. Each level states what it claims, what it does not, and where liability sits. A case earns its level from its own artifacts; a level is never granted.
The document has been received and stored in the system.
Does not claim the document is authentic or complete.
None beyond storage.
These values were extracted from the document text by the pipeline, each carrying a source page and quote.
Does not claim the values are correct or the document truthful.
Extraction quality only.
The extracted data is consistent across the documents provided and passes the internal recompute / reconciliation checks that ran.
Does not claim the underlying business reality matches the documents.
Cross-reference accuracy.
No statistical anomalies, fabrication signals, or duplicate-pledge patterns were detected in the data.
Does not claim the absence of sophisticated fabrication that the data patterns may not surface.
Methodology, not outcomes.
The full pipeline ran and every check passed or its warnings were acknowledged; the documents meet UMFR's verification standards under the stated methodology.
Does not claim investment suitability and does not constitute advice.
Per the disclaimer model.
Direct lenders and credit funds verifying collateral and borrower reporting before they commit — and monitoring it after.
PE-owned insurers and institutional allocators who must now demonstrate independent diligence as regulators tighten (NAIC's framework takes effect in 2026).
Fund administrators, ODD teams, and lenders' counsel who run diligence on others' behalf and need a verifiable, repeatable record.
UMFR is built by a founder with two decades of executive and board-level leadership in banking, financial services, and beyond — 15 institutional board seats across 8 jurisdictions.
At BTA Bank he served on the Management Board through the bank's $11.1B international debt restructuring — advised by Lazard and White & Case — worked at executive level on the recovery from the Ablyazov fraud, one of the largest fraud cases in English legal history, and supervised the bank's full group of subsidiaries across eight jurisdictions. A forensic audit had uncovered a ~$10B hole; the recovery — pursued through Hogan Lovells and White & Case with international forensic accountants, across multiple jurisdictions — returned over $6B across seven of them.
That fraud was found the only way it could be found then: forensically, after the collapse, by people reading documents against each other across jurisdictions. UMFR does the same reconciliation before — deterministically, on the source documents, every figure cited.
The VERIFY Score is a triage signal that tells a reviewer where to look. It is not a credit rating, and UMFR is not a rating agency.
UMFR surfaces what does not reconcile, for a human to review. It does not allege wrongdoing by any company.
Where a document or figure cannot be verified, UMFR says so. It never treats silence as clean — a coverage line states what ran and what did not.
The figures in a UMFR report are recomputed by deterministic logic and traced to source. No language model decides the numbers a reviewer sees.
The cases here run on public filings. Detecting the same asset pledged across different lenders needs a cross-lender network, which public data cannot provide (borrower identifiers are stripped by regulation). The $68.1Bsupplier-finance study maps a disclosure category across public filers — it is not a "First Brands detector" (First Brands was private and never appeared in these filings).
Reported and verified are not the same thing. UMFR is the independent verification layer for private credit.