Expose bank receipt forgery in seconds
Upload a receipt and we automatically audit dozens of forensic signals — from PDF producer and IBAN validation to date conflicts, re-rendering and AI traces — and return a definitive authenticity report.
What we detect
We surface tampering that the eye cannot catch, by examining the document’s digital structure at a forensic level.
PDF producer mismatch
Each bank compiles receipts with a known engine. An unexpected producer (e.g. browser re-render) flags forgery.
IBAN & bank-code validation
IBAN mod-97 checksum, sender–receiver IBAN consistency and receiver bank ↔ IBAN code matching are verified.
Date ↔ document-no conflict
The date embedded in the document number is compared with the transaction time; a mismatch exposes back/forward dating.
Re-render / PDFium trace
Producer signatures and incremental-update traces revealing a re-saved PDF are captured.
Font & amount anomalies
If the amount field’s font family differs from the rest, it points to digits altered after the fact.
AI & image forensics
For photo receipts, ELA, noise and background analysis assess editing and AI-generation traces.
How it works
From upload to a definitive verdict in four steps.
Upload
Drag & drop the receipt PDF or photo.
Forensic analysis
Structure, producer, IBAN, date and image signals are audited.
Definitive answers
Authenticity score, yes/no checks and a tampering comparison.
Act with confidence
Download the report or integrate via the API.
Cross-Receipt Analysis (Transaction Number ↔ Time)
Upload 2 or more receipts from the same sender together. A later transaction must carry a higher transaction/sequence number.
REST API for developers
Use the REST API to analyze documents programmatically.
GET /api/v1/health · POST /api/v1/analyze · /api/docs