Reviewing payment exceptions
Provides a focused report of quantity and price discrepancies outside agreed tolerances, helping accounts payable teams assess which invoices require investigation.
— Finance & Investment
Three-way match exception engine
Joins purchase orders, goods receipts and invoices on the PO number, derives quantity and price variances arithmetically, and cause-codes only the breaks that clear tolerance.
- Finance & Investment
- Traceable reasoning
- Runs anywhere
1–2 weeks → ~40 minutes
For a full population, not a sample
No coding required
- Input
- Purchase order records with purchase order numbers, ordered quantities and agreed prices
- Output
- Three-way match exception report
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~1–2 weeks per run
— USE CASES
What people use Three-way match exception engine for
Investigating supplier discrepancies
Explains mismatches across purchase orders, recorded receipts and invoices, with cause classifications covering short delivery, price uplift, duplicate invoice and unauthorised purchase order change.
Assessing purchasing controls
Gives finance and advisory teams a consistent view of three-way match exceptions, with calculated variances and explanations that support control reviews.
Supporting disputed invoice reviews
Sets out the quantity or price difference behind an exception, giving reviewers a factual basis for assessing the discrepancy alongside its proposed cause.
What it works from
- Purchase order records with purchase order numbers, ordered quantities and agreed prices
- Goods receipt records with purchase order numbers and received quantities
- Invoice records with purchase order numbers, invoiced quantities and invoice prices
- Quantity and price variance tolerances
What you get back
- Three-way match exception report
- Calculated quantity and price variances
- Exceptions outside defined tolerances
- Plain-language explanations of discrepancies with proposed cause classifications
— HOW IT BEHAVES
How Three-way match exception engine 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
Differences between orders, deliveries and invoices create payment risk and absorb finance teams’ time, particularly when routine variances obscure issues that need investigation. Reviewing payment exceptions is the typical trigger — provides a focused report of quantity and price discrepancies outside agreed tolerances, helping accounts payable teams assess which invoices require investigation. Get it right and the conclusion holds up in the room; get it rushed and it gets picked apart. Either way it costs roughly 1–2 weeks of experienced attention.
How this Skill produces it
As a Skill, the work is already sequenced. You bring the evidence, and the run produces three-way match exception report plus calculated quantity and price variances. The criteria, ordering and review points that make the answer trustworthy are encoded in the Skill itself — which is the difference between a structured method and a prompt someone pastes in. In effect, 1–2 weeks of senior time compresses into ~40 minutes — and the output is comparable across clients, quarters and colleagues instead of shaped by whoever ran it.
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
Three-way match exception engine 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)
Related Skill Templates
More Finance & Investment templates →Finance & Investment
Data Room to IC Memo
Turns a target’s data room materials into a source-cited Investment Committee memo, reconciling financial evidence and assessing deal risks to support a clear investment recommendation.
Finance & Investment
Financial Health Diagnostic
Computes period-on-period movement across a financial summary and reconciles profitability, liquidity, leverage, cash generation and working capital into a triaged read on strengths, risks and priority actions.
Finance & Investment
Budget consolidation
Rolls stacked department budget submissions up by cost centre and department, compares the total to your envelope, and writes the consolidation note with credible trims.
Finance & Investment
Credit / Loan Underwriting
Assesses a credit application against your own credit policy and returns a governed underwriting decision — approve, refer or decline — showing how affordability, risk and policy thresholds drove the outcome, and holding it for a credit officer sign-off before any letter is issued.
Finance & Investment
Profitability Analysis
Computes gross profit and margin by product, customer, unit or geography, isolates loss-making and sub-scale lines, and explains where value leaks through discounting, cost-to-serve, mix or small orders.
Finance & Investment
Investment Potential Analysis
Scores a public company's fundamentals alongside live market signals and research for near-term momentum, medium-term trajectory and long-term durability, reconciling all three into one investment-potential verdict.