Fintech CEO

Pre-Raise Unit Economics Audit Prompt

Run a rigorous unit economics self-audit to surface red flags investors will find first, with a fix-it plan and a diligence-ready summary.

Anselm Fowel / Public collection28 Sept 2026

Your prompt

The prompt

You are a fintech-focused financial diligence expert who has reviewed unit economics for dozens of fundraises and can spot the numbers that make investors quietly pass without ever saying why.

Our company is [COMPANY NAME]. Our current metrics are: CAC of [CAC], LTV of [LTV], gross margin of [GROSS MARGIN %], monthly burn of [BURN], and burn multiple of [BURN MULTIPLE]. Our revenue model is [DESCRIBE MODEL, e.g., interchange, subscription, take rate on payment volume].

First, calculate and sanity-check our CAC/LTV ratio and burn multiple against the benchmarks a Series [A/B] fintech investor would expect, and tell me plainly whether we are in healthy, borderline, or red-flag territory for each metric, explaining the reasoning an investor's associate would use.

List the 10 unit economics red flags that make sophisticated fintech investors walk away, such as CAC payback periods over 18 months, gross margin erosion from interchange compression, cohort LTV that hasn't been tested past 12 months, or revenue concentration in a handful of accounts. For each, tell me whether it applies to us based on what I've shared, and what data I'd need to check to be sure.

Write the explanation I should give investors for our weakest metric, one that acknowledges the issue honestly and shows the specific operational change already underway to fix it, since investors respect founders who diagnose their own numbers before being asked.

Build a 90-day action plan to improve our two weakest metrics before we go to market, with a specific weekly milestone for each.

End with a one-page "numbers behind the numbers" summary formatted for a data room, plus a red/yellow/green scorecard across CAC payback, gross margin, net revenue retention, burn multiple, and revenue concentration, so I know exactly what an investor will flag in the first five minutes of reviewing our model.

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