For finance & ops

Your fake-receipt
defense, scored.

AI image generators went from 0% to roughly 71% of flagged fake receipts in a year — visually perfect, and priced around $100 to duck your auto-approval line. Eyeballing is a dead control. Score your expense programs and find the gap before a fake walks through it.

Six controls, one verdict per program — CONTROLLED, SOFT SPOTS, or EXPOSED — with a gate built on the two controls AI receipts are engineered to beat. A program can score 76 and still read EXPOSED.
Get the Gate
$69one-time

instant download · .xlsx · yours to keep

The problem

The receipt stopped being proof.

0% → ~71%

the share of flagged fake receipts identified as AI-generated, March 2025 to May 2026. The crossover hit in April 2026 — AI is now the dominant fabrication tool.

~$100

the average AI fake, versus $182 for older template fakes. The lower amount is intentional: small enough to slip under most auto-approval thresholds and skip review.

4 in 10

US employees who admit using AI to generate a fake expense receipt — many with company-funded AI tools, per a June 2026 survey.

Any workflow built on “review the receipt, decide if it's real” is now obsolete — detection happens after the spend, and a perfect forgery passes a human reviewer. This kit grades the controls that don't depend on the image, and refuses to let a program pass while the two AI-proof controls are missing.

See it work

Score a program and watch the gate fire.

Score one program

Mark each control. The verdict updates live — same math as the workbook.

Source-of-truth cross-checkGatew24

Claims verified against card / bank / merchant data, not the receipt image.

Sub-threshold pattern monitoringGatew20

You watch the ~$100, just-under-auto-approval, repeat-small-claim pattern.

Prevention rails (virtual cards)w16

Virtual / corporate cards and direct feeds shrink the reimbursement surface.

AI-forgery / metadata checkw14

Metadata and AI-generation detection, not human visual review (AI receipts pass that).

Clear written policy & consequencesw14

A communicated policy stating the documentation standard and the consequence for a fake.

Approvers accountablew12

Approvers know they are a control, not a rubber stamp clearing a queue.

Verdict
EXPOSED
Score
76/100

Scores in the CONTROLLED band, but a gate control is at 0 — so the verdict is forced to EXPOSED. AI receipts are visually perfect and priced to duck the auto-approval line, so without an independent cross-check or sub-threshold monitoring, your other controls can't catch them.

Fix first: Source-of-truth cross-check

Your marks only · no benchmark · grades the controls, not people

The standard

Six controls, weighted to 100 — two of them gates.

Source-of-truth cross-check (card/bank/merchant)Gate
24
Sub-threshold & low-dollar pattern monitoringGate
20
Prevention rails (virtual cards / direct feeds)
16
AI-forgery / metadata authenticity check
14
Clear written policy & consequences
14
Approvers accountable as a real control
12

The gate (worsen-only): if source-of-truth cross-check = 0 or sub-threshold monitoring = 0, the verdict is EXPOSED regardless of the score. These are the two controls AI fakes are built to defeat — a perfect image beats any visual review, so you need an independent cross-check; and the fakes are priced to live below your review line, so you need something watching that band. Either alone is fatal. Raise it off zero and the gate releases.

How it works

Deterministic. Your marks, one verdict.

The workbook reproduces a runnable engine exactly. The Field-sales program below scores 76 and still reads EXPOSED — strong everywhere except the one control that catches a perfect fake.

AI-Receipt Fraud Exposure Gate
====================================================
Field sales T&E             76/100  EXPOSED       [GATE -> EXPOSED]
    fix first: Source-of-truth cross-check (card/bank/merchant data)
Corporate card + virtual   100/100  CONTROLLED
Contractor reimbursements   52/100  EXPOSED       [GATE -> EXPOSED]
    fix first: Sub-threshold & low-dollar pattern monitoring
Remote-team stipends        85/100  CONTROLLED
    fix first: Prevention rails (virtual cards / direct feeds)
Conference & events         50/100  SOFT SPOTS
    fix first: Source-of-truth cross-check (card/bank/merchant data)
Petty cash & misc           35/100  EXPOSED
    fix first: Prevention rails (virtual cards / direct feeds)
----------------------------------------------------
Portfolio: EXPOSURE FOUND
3 of 6 program(s) read EXPOSED.

Who it's for

Anyone who signs off on reimbursements.

Controllers and finance leaders owning the expense policy
AP and expense-management teams under FY26 fraud pressure
Founders/operators approving T&E without a big controls team
Internal audit running a controls self-assessment
Ops leads standing up virtual-card or direct-feed programs
Anyone who just learned eyeballing receipts no longer works

This is a control self-assessment, not a fraud audit or a detection tool — it scores the controls you describe from your own marks, connects to nothing, reads no receipt, and grades no employee. Not accounting or legal advice.

Pairs well with

The rest of the spend-integrity shelf.

Common questions

The honest answers.

Your expense-reimbursement controls — for one or more programs (T&E, contractor reimbursements, stipends, petty cash) — against the specific way AI fake receipts get paid. You mark six controls 0/1/2 and it returns CONTROLLED, SOFT SPOTS, or EXPOSED per program, with the one control to fix first. It scores the controls you describe, never an employee, and it catches no fraud itself — it tells you whether your process would.

Get the kit

Find the gap before a fake does.

  • One .xlsx — Start Here, Dashboard, Exposure Scorecard.
  • Score every expense program; rolls up to one portfolio verdict.
  • Deterministic and offline — your marks, no AI, nothing uploaded.
  • Opens in Excel, Google Sheets, or Numbers.
$69

one-time

Buy the Gate

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