Finance-LLM Diagnostic · Luxembourg

A finance-LLM diagnostic. Built by a finance director, not a benchmark.

I run your finance-domain model against a senior-practitioner test bank and return a catalogue of named, mechanism-level failure modes — what broke, where in the structure it broke, and how serious it is.

Not a score. Not a leaderboard. A structural post-mortem.

Sample findings

Three failures a senior reader recognises on sight.

Illustrative reconstructions — not real client work. Swipe or drag to read across.

Valuation Distressed & special situations
Applies a yield-to-maturity framework to distressed debt where a no-arbitrage recovery PV is required.
StructuralCritical

What the model producedAsked to value a bond on which the issuer has defaulted, the model computed a yield to maturity from the bond's scheduled coupons and par — treating the promised cash flows as the cash flows the holder will receive.

Why this is wrongOnce default has occurred, the promised coupons and par are not the expected cash flows. A defaulted bond is valued on its expected recovery — estimated workout or liquidation proceeds and their timing, discounted to present value — not on a yield to maturity computed from cash flows that will not be paid.

Downstream impactThe figure looks quantitatively coherent and would survive a typical citation/extraction review. Used as an input to a pricing or workout decision, it systematically overstates value, because it prices in coupons the holder will never collect.

M&A Purchase price allocation
Omits the deferred tax liability on the asset step-up in purchase accounting.
StructuralCritical

What the model producedAllocating purchase price in a stock deal with a non-deductible step-up, the model wrote fixed assets and intangibles up to fair value and plugged the residual to goodwill — with no deferred tax liability recognised.

Why this is wrongA step-up taken for book but not for tax creates a taxable temporary difference, and so a deferred tax liability equal to the step-up times the tax rate. That liability is itself a component of the allocation, and recognising it increases goodwill.

Downstream impactGoodwill is understated and net identifiable assets overstated, which overstates impairment headroom. And because the omitted liability never unwinds, deferred tax expense and net income are misstated across the asset lives.

Capital structure Preferred
Treats convertible-preferred liquidation preference and conversion value as additive rather than greater-of.
StructuralCritical

What the model producedIn an exit waterfall, the model paid the preferred holder its 1× liquidation preference and then also credited the full as-converted common value on the same shares.

Why this is wrongA non-participating preferred receives the greater of its liquidation preference or its as-converted value — not the sum. At any given exit value the holder elects whichever is larger; the two are mutually exclusive.

Downstream impactThe preferred claim is overstated and common proceeds understated across a band of exit values. Any return-to-common or management-incentive analysis built on the waterfall is distorted.

View the full failure taxonomy →
What a diagnostic returns

The right column is the credibility move.

What you get

  • A catalogue of named, mechanism-level failure modes found in your deployed model.
  • A type and severity rating on every finding — Structural, Arithmetic, Hallucination, Disclosure.
  • Per-subdomain findings, prioritised by severity weight.
  • Remediation framed as test cases you can add to your own regression suite.
  • An executive summary a senior reviewer can act on.

What you don't get

  • A leaderboard score, or a ranking against other models.
  • A regulatory conformity assessment or model-risk sign-off.
  • A black-box LLM-as-judge verdict on structural questions.
  • Your test items exposed — the bank stays private.
  • A pass/fail headline that hides where and how the model broke.
Who this is for
i

AI-first finance product teams

Shipping a finance-domain model into production, and wanting it to actually work where it matters.

ii

Model-risk & validation teams

Pressure-testing a vendor model as an input to your own validation work — not a substitute for it.

iii

Investors diligencing finance AI

Underwriting a finance-AI company and needing a senior read on where its model structurally breaks.

How an engagement runs

Four tiers. Pricing on the page.

One funnel. Every engagement begins with a free sample diagnostic.

See what each tier includes →
Finance-LLM audit — FAQ

Common questions.

What is a finance-LLM audit?

A finance-LLM audit is a structural evaluation of a finance-domain language model: you run it against a practitioner-built test bank and get back a catalogue of named, mechanism-level failure modes — what broke, where in the finance reasoning it broke, and how serious it is. It is a post-mortem of how the model fails, not a single score or leaderboard rank.

How do I audit or evaluate a finance-domain LLM?

Run the model against a bank of senior-practitioner finance questions spanning valuation, M&A, credit, capital markets and FP&A, then have a finance practitioner grade each answer for structural correctness — not just surface fluency. Audit LLM does this for you and returns a typed, severity-rated finding list. The fastest start is a free sample diagnostic on one subdomain.

Who audits finance LLMs?

Audit LLM is a single-practitioner diagnostic practice, based in Luxembourg, built on a Finance Director and FP&A background and the QA review of more than a thousand finance-domain LLM annotations — the source of its failure taxonomy. It is deliberately low-volume and independent: no leaderboard, no vendor relationships.

What kinds of failures does a finance-LLM diagnostic find?

Mechanism-level structural errors that survive a citation check but are wrong where it matters — for example omitting the deferred tax liability on a purchase-accounting step-up, applying a yield-to-maturity to a defaulted bond, or discounting unlevered cash flows with a levered beta. Findings span 23 finance subdomains and are rated by type (Structural, Arithmetic, Hallucination, Disclosure) and severity.

How is a finance-LLM audit different from a benchmark or leaderboard?

A benchmark gives you a score and a rank; a finance-LLM audit gives you a structural post-mortem — the specific, named ways your model breaks and why. It is built for the team deploying the model, not for marketing comparisons. Not a score, not a leaderboard.

How do I get a finance-LLM audit?

Request a free sample diagnostic: name one finance subdomain to sample and you get a one-page finding letter on a single item from the bank. From there, engagements scope up to a fixed scoping diagnostic or a comprehensive multi-subdomain evaluation.