Document Risk

Document Fraud Costs More Than You Think

Bank statement fraud alone accounts for 59% of all fraudulent documents in lending. One approved loan based on an edited PDF costs more than a year of fraud prevention tooling — and KYC platforms cannot see it.

The blind spot

KYC confirms the person. It never inspects the PDF.

Identity platforms confirm that the applicant is real. Template checks confirm that the document looks like a bank statement. Neither answers the question that actually matters: was this specific PDF edited after it was issued?

Modern document fraud does not redraw a logo. It opens a genuine, legitimately issued PDF, swaps a balance, a date, or an IBAN, and re-saves it. Visually nothing changes. The document passes pixel-level review, layout review, and KYC. The loss shows up months later as a charged-off loan or a redirected payment.

Structural PDF analysis reads the layers rendering engines never expose — revision history, object structure, signature coverage maps. That is where edits leave fingerprints they cannot wipe.

HTPBE? doesn’t replace your identity check — it adds a structural layer on top of it. Plaid and Persona confirm who is submitting the document; HTPBE? confirms the file itself wasn’t edited after it was issued.

$15K–$50K
Direct loss per approved fraudulent loan
59%
Of lending document fraud is bank statements
47.4%
Of 9,732 analyzed PDFs were modified
$127K
Average direct loss per fraud incident

Where the loss happens

Three places undetected edits cost real money

1

Lending and fintech

An inflated bank statement or pay stub turns a decline into an approval. The loan defaults, collections cost adds up, and the line item lands in the charge-off column at $15K–$50K per case.

2

Accounts payable

An edited invoice with a swapped IBAN routes payment to a fraudster while the PO matches and the supplier looks legitimate. Average direct loss per incident: $127K, rarely recoverable.

3

Hiring and compliance

Modified diplomas, salary slips, and offer letters pass pre-employment screening. Beyond a wrong hire, regulated industries face audit findings for missing structural integrity controls on intake documents.

What we see in production

The exposure is not theoretical

Aggregate across all PDFs analyzed by HTPBE? users. Each number is a document that passed visual and template review and was caught by structural analysis only.

15,303
PDFs analyzed across customers
47.4%
Of conclusively analyzed documents had been modified
24years
Oldest creation date observed in submitted PDFs

Updated automatically from production checks — see the live statistics dashboard →

Your identity stack confirms the applicant. HTPBE? confirms the file wasn’t edited after it was issued.

High-risk patterns

Three fraud patterns KYC platforms cannot catch

bank statement edit

Inflated income, removed overdrafts

A genuine PDF exported from a banking app, edited to inflate balances or hide overdraft history. Every visual cue is correct — the underlying numbers are not. Detected via incremental update chains and producer inconsistencies.

post-signature change

Contract modified after it was signed

The only fraud scenario where HTPBE? returns certain. A digital signature exists, and the document was modified after it was applied — the forensic evidence is unambiguous and defensible in dispute.

credential edit

Edited diploma, salary slip, or offer letter

HR and staffing platforms see modified academic certificates and altered employment letters that pass visual review. Structural analysis works without access to an original or to the issuing institution’s database.

Exposure by document type

What is at stake in each category

The same forensic API runs across all of these. Each card lists fraud patterns we regularly catch in production traffic.

4

Bank statements

  • Inflated balances and salary credits
  • Removed overdraft history
  • Shifted transaction dates
  • Replaced merchant names
4

Invoices and POs

  • Swapped IBAN or beneficiary
  • Edited line items and totals
  • Backdated issue dates
  • Forged supplier letterhead
4

Contracts

  • Modifications after signature
  • Removed digital signatures
  • Altered counterparty terms
  • Inserted clauses post-execution
4

Pay stubs and salary slips

  • Inflated gross income
  • Modified employer name
  • Edited tax and deduction lines
  • Forged year-to-date totals
4

Credentials and diplomas

  • Altered grades and dates
  • Modified institution names
  • Forged transcript layouts
  • Edited offer letter terms
4

Tax and government forms

  • Edited reported income
  • Modified filing status
  • Removed prior-year liabilities
  • Forged agency stamps

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