Catch fake and edited supporting documents before claims are paid.
Detection layers are weighted for the document mix your industry actually reviews — IDs, statements, paystubs, invoices, leases, and policies.
Inspects EXIF, software signatures, edit history, and structural fingerprints.
Originals are processed in encrypted memory and removed after analysis. Reports stay redacted by default.
Claim fraud is one of the highest-loss categories in insurance, and AI tools have made fake supporting documents fast to produce.
TrueDoc inspects the receipts, invoices, statements, and proofs attached to claims — surfacing tampering and AI-generation signals before payout.
Edited receipts and invoices with inflated amounts
AI-generated repair, medical, or replacement invoices
Faked proof-of-loss photos and screenshots
Duplicate documents submitted across multiple claims
Upload via your claims platform or send via API.
Math, metadata, layout, ELA, and AI-generation signals.
Auto-approve clean claims; route high-risk claims to SIU with evidence attached.
Every verdict is logged with evidence for regulator and reinsurer review.
Claims handlers and adjusters typically see the same three failure modes: submissions that look professional but were assembled from a template, real documents recycled from a prior application with edited fields, and fully AI-generated files that no longer trip rule-based checks.
The hardest of those is the second — recycled real documents — because the underlying file is genuine. TrueDoc looks at submission lineage and pixel-level evidence, not just whether the document "looks real."
TrueDoc is built to sit alongside your current process, not replace it. A typical rollout: documents land in your existing intake (CRM, LOS, ATS, or portal), TrueDoc returns a verdict and per-field evidence via API or dashboard, and your reviewers spend their time on the cases the model isn't confident on.
That keeps the claims handlers and adjusters accountable for the final decision while removing the obvious-good and obvious-bad cases from the queue.
Two loss patterns dominate: edited receipts and invoices with inflated amounts, and ai-generated repair, medical, or replacement invoices. The first is loud — a single application that goes wrong. The second is quieter and more expensive: the same fabricated document type re-used across many submissions before anyone connects the cases.
Both show up in the per-finding evidence TrueDoc returns. Teams that review the recycled-document patterns weekly tend to catch organised submitters earlier in the lifecycle.
No credit card. Redacted report in under a minute.