UMFR independently re-computes what borrowers report and flags what doesn't reconcile against the source documents -- with every figure traced back to its origin. Built for the lenders, insurers, and allocators who carry the risk.
Private credit has grown from roughly $400B in 2015 to about $3.5 trillion today -- and is projected to reach $5 trillion by 2029. It is now a systemic asset class. Yet it still runs on PDFs, spreadsheets, and trust.
In late 2025, that caught up with the market.
Collapsed with more than $10B in liabilities and roughly $2.3B in factoring obligations that sat off the main balance sheet, now under court examiner.
Failed with around $800M in exposure tied to the same auto collateral pledged to multiple lenders.
Both were diligenced. Both reported clean.
The lesson the market drew: reported and verified are not the same thing.
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 doesn't reconcile as exceptions for review, with every figure traced back to the exact line in the source. A reviewer confirms in seconds.
Re-compute the figures behind the loan -- earnings quality, EBITDA add-backs, covenant compliance -- and flag what doesn't add up. Proven on real SEC filings and comment-letter cases.
Cross-check reported obligations against what the same filings actually disclose. In one public-market study, UMFR mapped $68.1B in disclosed supplier-finance obligations across about 140 filers.
Test loan-level pools for the fabrication and within-pool manipulation patterns behind the 2025 failures -- proven across 346,646 real loans.
UMFR's engine doesn't do one trick on one asset class. It has run -- deterministically, on real public data, at zero compute cost -- across the full range of how private-credit reporting fails:
Seed synthetic fraud into the same pools, and every signal trips. The engine separates authentic collateral from fabricated -- deterministically, at zero compute cost.
Multiple real SEC filings and comment-letter cases where reported "adjusted" earnings were rebuilt and questionable add-backs surfaced for review.
Recomputed from the underlying figures on a real 10-Q (Flow International), rather than taking the borrower's word.
A $68.1B disclosed-magnitude map across about 140 public filers -- and inside it, a precision core: of the 18 filers that tag the full XBRL rollforward, the engine closed the arithmetic exactly on 9 and reconciled 14in total, surfacing the rest for review. It doesn't just size the exposure -- it checks whether the numbers actually tie out.
A secured-lien chain cross-checked against the UK Companies House registry (Greensill Capital) -- corroborating reported security against an independent public record.
Different issuers, different credit types, one engine -- and every figure traces back to its source.
Not a roadmap promise -- these are properties of the engine as it runs now.
The same inputs always produce the same result. Reproducible and auditable -- a defensible answer for a regulator or credit committee, not a black-box score.
Every figure links back to the exact line in the source document. Hours of manual reconciliation become seconds of review.
Runs on real public data at zero compute cost -- no dependence on expensive infrastructure or per-query model fees.
Zero false positives across 346,646 real loans -- low enough noise to sit inside a real workflow, not just a demo reel.
Most verification happens once, at origination, and then goes stale. Because UMFR's engine is deterministic and costs nothing to run, the same check can repeat continuously -- a standing check on an instrument over its life, not a one-time look. (Continuous monitoring as a live service is on the roadmap.)
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.
Verification is the entry point, not the destination. Public markets long ago built the infrastructure that makes them trustworthy and liquid -- independent verification, a central registry, standardized reporting, and a place to trade. Private credit never did. UMFR is building that infrastructure, one proven layer at a time.
Independently verify what borrowers report, against the source documents.
A machine-readable registry of verified instruments, queryable as a single source of truth.
Turn verified data into the regulatory and compliance reporting lenders and insurers must produce.
Independent credit assessment and indices built on verified data.
Integrate verification into the systems institutions already run on.
A neutral venue where verified instruments can change hands.
The verification, registry, and settlement infrastructure that public markets take for granted -- built for private credit. Each layer makes the next more valuable, because verified data compounds.
UMFR is built by a founder who spent two decades on the lending side of these deals -- at management-board and board level across banking, credit, and financial institutions, including the workout of an $11B cross-border debt restructuring. The problem UMFR solves is one its founder spent a career living.
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 doesn't reconcile, for a human to review. It does not allege wrongdoing by any company.
On public securitization data, UMFR verifies authenticity within a disclosed pool. Detecting the same asset pledged across different lenders requires a cross-lender network, which public data cannot provide (borrower identifiers are stripped by regulation). That neutral network is on the roadmap.
UMFR's $68.1B supplier-finance study maps the class of off-balance-sheet leverage implicated in 2025's failures, 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.
Contact the founder -> founder@umfr.io