Loan & Insurance Document Forgery

Automated forgery detection reads the structure a human reviewer cannot see: fonts and alignment that drift from the bank's real template, edited regions whose noise and compression history differ from the rest of the page, and the statistical fingerprints of AI-generated documents. ScamAI scores each statement and returns a confidence score with localized evidence — inside the origination flow via the REST API or in batch across a portfolio. Cross-application scoring exposes the same doctored template under different applicant names.

Yes. Pay stubs, employment letters, and proof-of-address documents are edited or generated with the same tools as any other document, and they leave the same structural evidence: template inconsistencies, mismatched edit regions, and generation artifacts. Detection returns a probabilistic score with the signals that fired, localized to the suspicious area, so an underwriter reviews the flagged field rather than the whole file. Because results are threshold-based, you decide which scores auto-route to review and which pass without friction.

At claims intake, every supporting document — invoices, repair estimates, policy paperwork, proof of loss — can be scored before adjudication begins. Genuine claims pass through without added friction; suspicious documents surface with a confidence score and localized evidence attached, so adjusters spend their time on the files that need judgment. Batch endpoints also let teams re-screen historical claims, and cross-claim analysis flags documents and templates reused across supposedly unrelated claims — a common pattern in organized claims fraud.