Testing seller earnings claims
Assesses how defensible proposed EBITDA adjustments are and highlights items that may overstate sustainable earnings. The findings support challenges to the earnings basis used in valuation.
— Finance & Investment
Financial Due Diligence
Computes customer concentration and adjustment totals from the EBITDA bridge and revenue file, judges how defensible each adjustment is, and names the earnings-quality and cash-conversion issues that should move price or structure.
- Finance & Investment
- Traceable reasoning
- Runs anywhere
3–4 weeks → ~40 minutes
For a full population, not a sample
No coding required
- Input
- EBITDA bridge or adjustment schedule with adjustment descriptions, types and values
- Output
- Financial due diligence findings report
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~3–4 weeks per run
— USE CASES
What people use Financial Due Diligence for
Assessing customer dependency risk
Quantifies revenue concentration and examines the contribution of key customers to gross profit. The findings clarify where customer dependency creates exposure for the buyer.
Informing deal price discussions
Identifies earnings and concentration issues relevant to price or transaction structure. It separates supported findings from concerns that need additional financial evidence.
Framing investment committee concerns
Provides a focused account of earnings quality, unusual adjustments and material deal risks. It makes limitations in the evidence, including the basis for cash conversion conclusions, explicit.
What it works from
- EBITDA bridge or adjustment schedule with adjustment descriptions, types and values
- Customer-level revenue and gross profit figures
- outputs
What you get back
- Financial due diligence findings report
- EBITDA adjustment totals and defensibility assessment
- Customer revenue concentration and gross profit analysis
- Quality of earnings findings and unusual adjustment concerns
— HOW IT BEHAVES
How Financial Due Diligence produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Every row of the reporting period in scope
Each row of your export is processed on the same basis, so no line item is skipped however long the table is.
The pattern charted
Movement across the reporting period in scope is visualised from the computed data, so the trend is legible at a glance.
Composed as work product
Findings on the reporting period in scope are written up as a document that reads like professional output, with each claim tied back to a line item.
Each line item enriched in place
Derived columns are added row by row, keeping your source data and the judgement about each line item side by side.
Roll-up alongside line item-level detail
Detail rows are summarised into the grouped view of the reporting period in scope without losing the underlying line items.
Why this is expensive by hand
Financial due diligence often leaves deal teams reconciling reported earnings with seller adjustments while assessing how much revenue depends on a small customer base. In practice it shows up as testing seller earnings claims: assesses how defensible proposed EBITDA adjustments are and highlights items that may overstate sustainable earnings. The findings support challenges to the earnings basis used in valuation. It is the kind of work that decides whether a recommendation survives scrutiny — and the kind that quietly eats 3–4 weeks of senior time whenever it comes round.
How this Skill produces it
Skillsize turns that work into a Skill: you supply the material, and what comes back is financial due diligence findings report, with eBITDA adjustment totals and defensibility assessment. The judgement is built in — how items are broken up, what standard they are held to, and where the run stops for a human review. The practical effect: 3–4 weeks of manual work becomes a ~40 minutes run, held to an identical standard on the tenth engagement as on the first.
Who it's for
- Finance directors and FP&A teams
- Investment, deal and corporate development teams
- Controllers and reporting managers
- Consultants building or reviewing business cases
Run it in Skillsize — or export it anywhere
Financial Due Diligence exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
ChatGPTClaudeCopilotAI Products (MCP)
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