Document Forgery

If your AP or KYC team has ever approved a payment, or onboarded a customer, on a document that turned out to be doctored, you already know the review process wasn’t missing effort. It was missing the kind of character-level and metadata checks a human eye cannot reliably catch at volume. That gap is what document forgery detection exists to close.

What Is Document Forgery?

Document forgery is the act of falsifying, altering, or fabricating a document, or a signature on it, with the intent to deceive, defraud, or misrepresent its authenticity.

Put simply: it is any document that looks real but was made or changed to lie about something, whether that’s an amount, an identity, or an approval.

As WebID Solutions defines it: “The falsification or alteration of a document or the production of a false document.” (WebID Solutions, 2026)

Detection systems typically route a suspected forgery based on a confidence score (planned entry) rather than a hard yes/no call. The three main methods:

• Manual alteration: modifying a genuine document, such as changing a figure on a real bank statement or swapping a photo on a real ID
• Counterfeiting: creating an entirely fake document from scratch, sometimes using stolen authentic materials or security features
• Digital manipulation: editing a PDF, invoice, or scanned image with software to change text, numbers, or a signature

How Document Forgery Detection Works

Detection depends on the intelligent character recognition layer reading the document accurately before any check downstream can trust what it finds.

Step	What it checks	Catches
Visual inspection	Fonts, spacing, alignment, color consistency	Manual alterations, mismatched fields
Metadata analysis	Creation date, edit history, software fingerprints	Digitally manipulated files
Security feature check	Watermarks, holograms, microprinting	Counterfeits missing authentic security features
Cross-reference	Extracted fields checked against other systems or documents	Numbers that don’t match anywhere else

Document Forgery vs. Document Fraud

These get used interchangeably, but they aren’t quite the same thing. Forgery is making or altering the document. Fraud is the broader crime of using deception, forgery included, for financial or personal gain.

Term	What it is	Example
Forgery	Creating or altering a document	Changing the amount on a real invoice
Fraud	Using deception for gain, forgery included	Submitting that changed invoice for payment

A forged document isn’t automatically fraud until someone tries to use it. That distinction matters for how automated document verification is scoped: catching the forgery earlier stops the fraud from happening at all.

Why Document Forgery Matters for Compliance and AP Teams

For a compliance officer or AP manager, document forgery is not a rare event. It’s a background rate baked into every intake channel. It shows up in four buyer metrics:

• Error and exception rate: forged documents are the exception category with the highest downstream cost if missed
• Cost-per-document: manual visual review does not scale to the volume most teams process
• Compliance exposure: onboarding a customer on a forged KYC document creates regulatory risk that outlasts the transaction
• It’s also why fraud checks are built into KlearStack’s BFSI deployments as a default, not an add-on

See how KlearStack flags a doctored document before it reaches approval.

Document Forgery Benchmarks

Financial institutions aren’t seeing this problem shrink. 57% lost more than $500,000 to fraud in 2023, and one in four lost over $1 million. (Alloy, 2024)

• Banking-sector fraud losses: projected to grow from $23 billion in 2025 to $58.3 billion by 2030 (Chargebacks911, 2026)
• Review time with automated detection: cut from 10-15 minutes to about 72 seconds per document (Inscribe, 2026)

That speed only helps if the exceptions it finds actually reach a person. This is the same queue human-in-the-loop processing (planned entry) is built to manage.

$500,000 a year is the average loss for more than half of financial institutions. Talk to us about closing that gap.

Common Mistakes and Limitations

Forgery detection breaks down in a few predictable ways.

• Visual-only review: a human catches obvious alterations but misses metadata tampering that doesn’t show up on screen
• One-time verification: a document gets checked at intake but never re-checked if it resurfaces later in the workflow
• No cross-referencing: a forged number that’s internally consistent but doesn’t match any other system goes unnoticed, a gap named entity recognition (planned entry) is built to close
• Treating detection as optional: teams add it after a loss, not before one

Real-World Example

Worked hypothetical, not an audited case study. A BFSI compliance team onboarding a customer receives a bank statement with a digitally altered balance.

• Metadata analysis flags a mismatch between the stated creation date and the software fingerprint in the file
• The mismatch routes to a reviewer instead of auto-approving the account
• This depends on the same document field mapping (planned entry) that routes every other extracted field, just applied to a fraud check instead of a schema match

If a forged document has ever slipped past intake and only surfaced at audit, you already know what that cost. Let’s close the gap.

Conclusion

Document forgery is not a rare, dramatic event. It is a steady background rate that shows up in ordinary intake volume, and it gets expensive precisely because it looks ordinary until someone checks the metadata or cross-references a field that doesn’t add up. Visual review alone was never built to catch what a font mismatch or a stripped edit history reveals.

For KlearStack’s buying committee, the real question is not whether forged documents are entering the pipeline. They already are. The question worth asking is whether anything downstream of intake, whether that’s metadata analysis, cross-referencing, or a properly tuned confidence threshold, is actually built to notice before the cost shows up at audit instead of at approval.

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