TamperCheck, the #1
FraudFinder Alternative

Three seconds is a real advantage in a synchronous flow, and it's also the whole shape of the trade. A three-second verdict is one pass over the file: fire the indicators, total the score, return it. There's no room in three seconds for a second look at what fired - so whatever the indicators produce, false positives included, is what lands in the analyst's queue. TamperCheck spends most of its minute on a QC pass over its own findings, re-validating each against the rest of the document before it's returned. Speed is cheap to advertise and expensive to act on: the real cost of a fast answer is the time someone spends disproving it.

FraudFinder's published method is a surface method. Their technology page describes examining every element of a file - font, barcodes, metadata - matched against trained templates (95%+ of UK and Irish bank statements, 98%+ of energy bills) and scored across 350+ fraud indicators. Against a fraudster who opens a real PDF and retypes a number, that works: the edit leaves metadata, resave history and structural residue to find. The threat model has moved. A document generated end-to-end is template-correct by construction, carries clean metadata because it was written once, and has no edit history because nothing was edited. Every signal a surface check depends on is absent - not because the check is badly built, but because there's nothing on the surface left to find.

That's why TamperCheck resolves the analysis to the individual field rather than the document. Each OCR field is compared against the rest of its own page: font weight, kerning and glyph rendering, edge sharpness, recompression history, black-floor level. A generated document has to survive that comparison internally, with no genuine reference page to imitate - a far harder problem than matching a template. A separate AI-generation and deepfake pass runs on every upload alongside it. FraudFinder does claim checks against ChatGPT-generated images and known template farms - a signature-style defence that catches yesterday's generators and lags the model released this month.

The coverage consequence follows from the same design. A trained-template system is only as good as its library: strong on UK and Irish formats, requiring training before a new issuer or country is covered. A template-free forensic system reads the file's own internal consistency, so a first-time Brazilian tax notice is analysed on the same footing as a Barclays statement. Their breadth is sideways - extraction, affordability, IBAN checks, AML - which is genuinely more product than we sell. Ours is downward, into the file.

Finally, what happens to the document afterwards. FraudFinder's published terms provide for Client Data retention of up to six years unless earlier deletion is requested, with on-demand deletion on Enterprise plans, UK/EU hosting, and a clause permitting anonymised and aggregated Client Data to be used to develop their models. TamperCheck never writes the document to disk and never trains on it. Retained data is what lets a template library grow, so the trade is a real one - but it's a trade your security reviewer gets to see, and it decides some purchases on its own.