A one page checklist of 15 places a deal model hides risk, written for PE deal teams and investment committees who run diligence before they commit capital.
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A deal model is a story told in numbers. The seller wrote it. Your job is to find the line where the story stops being true. The new AI models read a data room in an afternoon and they are very good. They are also, sometimes, confidently wrong. So this checklist works on both: the 14 places a deal model bends the truth, and the one place a brilliant model hands you a clean answer that is dead wrong. Use it as a gate. Every target. Before the investment committee, not after. ## Earnings quality (is the EBITDA real?) 1. **Kitchen-sink addbacks.** "Adjusted" EBITDA carrying costs that come back every year. Demand a bridge from reported to adjusted and challenge every line over 2% of EBITDA. 2. **Run-rate revenue off one good quarter.** Annualizing the best three months. Ask for the trailing twelve and the quarterly trend, not the annualized snapshot. 3. **Revenue pulled forward into the sale.** Bookings that spike in the two quarters before a process. Compare deferred revenue and billings to recognized revenue across the run-up. 4. **Synergy addbacks that have not happened.** Strip every unrealized synergy out of the entry number. Pay for what exists, not what is promised. ## Cash and working capital (does the EBITDA convert?) 5. **EBITDA that never becomes cash.** A widening gap between EBITDA and free cash flow is the single most reliable warning in the book. Track the conversion rate over three years. 6. **Working capital normalized to a flattering point.** Stretched payables and pulled receivables before sale inflate the cash that looks free. Use a normalized, seasonally fair level. 7. **Capex understated, maintenance disguised as growth.** Ask what spend is required just to hold revenue flat. That is the real floor. 8. **Deferred revenue masking a declining book.** Look at new logos and gross bookings, not just recognized revenue. ## Leverage and covenants (does it survive a bad year?) 9. **Covenant headroom modeled off the adjusted number.** Re-run every covenant on reported EBITDA and on the downside case. 10. **Refinancing assumed at today's rates for the whole hold.** Stress the rate path. Ask what returns look like if the cost of debt is 200 to 300 bps higher at refi. 11. **PIK and springing terms that look benign until they do not.** Model them in the bad case, where they actually bite. ## Projections and exit (is there a margin of safety?) 12. **The hockey stick with no history behind it.** Demand the operational reason for the year-three inflection. If it is "the market," it is a wish. 13. **Exit multiple at or above entry.** Underwrite the deal on multiple compression and see if it still clears. 14. **A downside case that is not a downside.** If the "bad case" still returns capital, it is a second base case. Build a real one: revenue down, margin down, multiple down, rates up, at the same time. ## The 15th: where the AI lies A capable model reads all fourteen faster than any associate. On a hard deal it will also: trust the seller's framing because the narrative is coherent (coherent is not true); extrapolate a trend off thin data and state it with the confidence of a fact; miss a contradiction between two documents in the room; and fabricate a tie-out, stating two figures reconcile when they do not. The fix is not a smarter model. Everyone has the same model now. The fix is a layer on top whose only job is to check the model against the evidence, flag every number that does not tie to a source, and refuse a verdict until the contradictions are resolved. That layer is the difference between a model that is usually right and a verdict you can put capital behind. ## Want this run on a deal you already know? We build the full engine into your fund, in your own environment, your deal data never leaves the building. Then we prove it on a deal you pick. The first one is free. Reply DILIGENCE or send a message, and we will run it.
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A one page checklist of 15 places a deal model hides risk, written for PE deal teams and investment committees who run diligence before they commit capital.
The setup rail and every prompt above are free and stay free. The cost is your time, and the risk of wiring it wrong on live data.
Back to the prompts ↑We build the full diligence engine into your fund, in your own environment, your deal data never leaves the building: the model, the data-room read, the quality-of-earnings and downside work, and the verification layer whose only job is to catch the model when it is confidently wrong. Then we prove it on a deal you already know. The first one is free.
Book a build call →Authored by consultance.ai. Deloitte and the named buyouts are referenced as public examples; no affiliation implied. The blind re-underwrites are retrospective re-underwrites where the model never saw the outcome, not timestamped predictions. This is decision support and a screening aid, not an audit opinion, a fairness opinion, or investment advice; a named human owns the call.
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The Deal Model Red Flag Checklist is a finance and data build in the consultance.ai AI Build Library. A one page checklist of 15 places a deal model hides risk, written for PE deal teams and investment committees who run diligence before they commit capital. It fits PE deal teams, family offices, VCs, credit investors, and investment committees who put real capital behind a diligence call. Setup difficulty is Medium, with 4 plain-English steps.
A one page checklist of 15 places a deal model hides risk, written for PE deal teams and investment committees who run diligence before they commit capital.
It fits PE deal teams, family offices, VCs, credit investors, and investment committees who put real capital behind a diligence call.
Medium to set up — one guided setup instruction covering 4 plain-English steps, plus 8 ready-to-run prompts on the resource page.
We build the full diligence engine into your fund, in your own environment, your deal data never leaves the building: the model, the data-room read, the quality-of-earnings and downside work, and the verification layer whose only job is to catch the model when it is confidently wrong. Then we prove it on a deal you already know. The first one is free.
Authored by consultance.ai. Deloitte and the named buyouts are referenced as public examples; no affiliation implied. The blind re-underwrites are retrospective re-underwrites where the model never saw the outcome, not timestamped predictions. This is decision support and a screening aid, not an audit opinion, a fairness opinion, or investment advice; a named human owns the call.
The Deal Model Red Flag Checklist is the starting point. On a free AI audit we map where it fits your stack and what consultance.ai would build around it.
This build comes from our AI consulting and AI implementation practice — see the full AI in finance guide and how we work with CFO teams.