Estimate how much undetected document fraud is costing your business each year.
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Estimated savings based on replacing a 10–15 minute manual document review with automated TrueDoc analysis.
Proprietary detection scans template variance, metadata drift, pixel-level retouching, and structural anomalies the human eye misses.
Inspects EXIF, software signatures, edit history, and structural fingerprints.
Originals are processed in encrypted memory and removed after analysis. Reports stay redacted by default.
Most teams underestimate document fraud because losses are spread across charge-offs, evictions, voided contracts, and clawbacks — never aggregated under a single line item.
A simple model — volume × fraud rate × average loss — usually surfaces a six- or seven-figure annual exposure, often justifying automated verification on its own.
Loan defaults driven by fake income docs
Tenancy losses from forged pay stubs and IDs
Insurance payouts on tampered claims
Vendor payments routed to fake invoices
KYC failures triggering regulatory fines
How many documents do you review per month?
Industry baselines: 1–3% lending, 3–6% rental, 5–10% insurance claims.
Include direct loss + recovery and operational cost.
Multiply through to a yearly number — then compare against verification cost.
Most "AI detector" tools look at one signal — usually a perplexity score on extracted text. The document fraud risk calculator runs that as one layer of many. It also evaluates metadata lineage (software, edit history, geo), pixel-level forensics (ELA, font kerning, retouching regions), and structural anomalies in the underlying PDF or image container.
The reason: loan defaults driven by fake income docs rarely leaves only one fingerprint. A convincing forgery usually fails on two or three of those layers, even when one of them looks clean.
A high-risk verdict on Income proofs (pay stubs, statements), Identity documents (IDs, passports), Invoices and receipts returns per-field evidence — not just a score. You see the suspicious regions highlighted on the page, the specific metadata fields that triggered the flag (for example, "Tenancy losses from forged pay stubs and IDs"), and the layer each finding came from.
That structure is what makes the verdict actionable: risk and fraud leaders can read why a document was flagged before deciding to reject, request a reupload, or escalate.
Scanned originals, mobile-camera shots, and re-exported PDFs are the three most common sources of benign anomalies. The document fraud risk calculator scores those differently from the patterns associated with deliberate forgery — for example, a recompressed JPEG from a phone is not treated the same as a recompressed JPEG with a font substitution.
When a document is flagged, the report tells you which signal triggered it. If the only signal is a low-confidence compression artifact, the verdict is downgraded rather than counted as fraud.
No credit card. Redacted report in under a minute.