Ocrolus alternative for document fraud: a structural PDF check that sits next to your OCR stack
OCR-based document analysis reads what is printed on the page. A fabricated bank statement created in Microsoft Word reads correctly – the numbers are right, the layout looks real, the extracted data passes cash-flow thresholds. What OCR cannot read is the file’s structural layer. HTPBE? reads that layer. Pair it with your existing extraction stack.
HTPBE? analyzes the structural layer of the PDF file only – how it was built and what happened to it afterwards. We don’t extract data, we don’t read text content with OCR, we don’t classify transactions or build cash-flow profiles. Ocrolus has those layers and a customer base; HTPBE? is positioned for teams that want structural fraud detection as a focused primitive, separate from extraction and analytics.
Who this is for
A structural check for teams that already have extraction – or do not want to buy it
HTPBE? fits lenders, mortgage operations and fintech engineering teams that receive bank statements, pay stubs and tax forms as PDFs and want to know whether the file was edited or produced by software that does not match its source. It is an API you call per file, with public pricing and test keys, so a developer can wire it in without a procurement cycle.
It is a poor fit if you want one vendor to extract the data, classify transactions, build cash-flow profiles and review exceptions for you. Ocrolus is built for that scope; HTPBE? does only the file-level check and says so.
HTPBE? fits when…
- 1Applicants upload PDFs and you need a file-level verdict before underwriting
- 2You already run extraction, or build your own, and want a separate integrity check
- 3You want per-check pricing and test keys without a sales call
- 4You need named markers your fraud team can read and act on
- 5You do not need extraction, categorisation or cash-flow analytics from the same vendor
What this looks like
How structural fraud survives OCR-based document analysis
Three real fraud mechanics we catch at the structural PDF layer.
Bank statement fabricated in Word – OCR reads it correctly
Applicant creates a bank statement layout in Microsoft Word using the bank’s logo from the web, types in three months of fictitious deposits totalling $6,400/month, exports to PDF. OCR extracts the amounts correctly. Cash-flow analytics shows regular income. Every layer that reads the document as text accepts it. HTPBE? does not clear it as an original: with nothing indicating later editing it comes back INCONCLUSIVE – not INTACT – and goes to manual review; with evidence of editing or fabrication, MODIFIED.
Pay stub edited to raise gross pay – OCR reports the inflated number
Applicant downloads a real Gusto pay stub showing $3,200/month, opens it in an editor, raises the gross pay to $5,800, saves. OCR extracts $5,800 and cash-flow analysis accepts it. The file itself carries structural evidence of the edit made after the original Gusto export – evidence OCR has no mechanism to detect.
W-2 with wages changed after IRS e-file – OCR cannot see the timestamp gap
A real W-2 from TurboTax shows $42,000 in Box 1. Applicant opens it months later, changes to $68,000, saves. OCR extracts the new figure. The modification date is 4 months after the creation date – a structural signal only forensic analysis reads.
How HTPBE? is positioned
Why OCR-based document analysis has a structural blind spot
Every OCR platform in the market reads what is in the document. None of them read whether the document itself is real.
The structural layer – where edits leave traces – exists in the binary file, not in the text.
OCR platforms (AWS Textract, Google Document AI, Azure Form Recognizer, Ocrolus) extract text and numbers from the document as rendered. They have no mechanism to inspect the binary file structure: whether the file is an untouched original and whether it was edited after issuance. A fabricated bank statement and a genuine Chase online export can produce identical OCR output – the difference, when there is one, sits in the structural layer. HTPBE? reads that layer as a standalone API call that sits alongside whatever extraction stack you already use.
Comparison
Why teams pick HTPBE?
A structural integrity check on the file versus an OCR platform that reads the content – they answer different questions.
| Feature | HTPBE? | OCR / document-automation platforms (e.g. Ocrolus) |
|---|---|---|
| Deployment model | Self-serve REST API – sign up, get a key, ship today | Managed platform with onboarding and account setup |
| Pricing and onboarding | Public pricing on /pricing – free test keys on every plan, no sales call | Sales-led engagement |
| Method | Structural file forensics – no OCR, reads the binary layer | OCR extraction plus transaction classification and analytics |
| On an edited statement | Flags edits saved after issuance and dates that disagree | Reads the (edited) numbers correctly – integrity is invisible to OCR |
| Output | INTACT, MODIFIED or INCONCLUSIVE with named markers – no extracted data | Extracted data, classified transactions and cash-flow analytics |
| Scope | Focused structural-forensics primitive – pairs with any OCR stack | Bundled OCR, classification, and cash-flow suite |
Document types
PDFs we analyze structurally for lenders and mortgage ops
Every type listed below is analyzed at the structural file layer – not the rendered image.
What HTPBE? checks
Detection capabilities
Deterministic structural signals. No probabilistic scores, no model training.
Office-built statements go to review
A bank statement, pay stub or tax form saved as PDF from Word, Excel or LibreOffice does not come back INTACT, however closely it copies the issuer’s layout. With no sign of later editing it returns INCONCLUSIVE for manual review; with evidence of editing or fabrication, MODIFIED – no OCR needed to make this call.
Post-issuance edit detection
Editing a PDF after issuance often leaves a structural trace in the file itself. HTPBE? detects these post-issuance edits at the structural level – invisible to OCR-based extraction.
Character overlay on the original
Changing a balance or a transaction row on an existing statement leaves a structural trace. HTPBE? flags it without reading the figures – it never looks at the numbers on the page.
Signature tamper detection
Tax forms, employer letters, and many institutional PDFs carry digital signatures. HTPBE? detects edits made after signing and flags signatures that were removed – orthogonal to whatever OCR sees in the text.
Pasted-element detection
HTPBE? detects logos, signatures and headers lifted from another document and pasted in – paste operations OCR cannot see.
Scan authenticity check
Some closing disclosures and bank statements are submitted as scans. HTPBE? flags scan-like PDFs that were actually generated by software rather than captured from a physical scanner – a distinction OCR does not evaluate.
Share with engineering
Wire this into your intake pipeline in under a day
Two API calls – one POST to submit the PDF, one GET to retrieve the verdict. Forward this page to your engineering team; the full API reference, quotas, and copy-paste examples in cURL, JavaScript, Python, PHP, Go, and Ruby are one click away.
Pricing
Self-serve plans, no sales call
All plans include the same forensic checks. Pick the quota that matches your monthly document volume.
manualStarter
30 credits/mo
Manual spot-checks and integration testing
most commonGrowth
350 credits/mo
Active document processing pipelines
high volumePro
1,500 credits/mo
High-volume automation and API integrations
Enterprise (unlimited, on-premise available) — see full pricing
API key on signup. Free test environment on every plan. No card required.
Customer Stories
Teams that stopped document fraud
Compliance, finance, and risk teams use HTPBE? to catch manipulated PDFs before they become costly mistakes.
Caught an invoice where the total had been changed by less than a thousand dollars. Without this I would have approved it without a second look.
Sarah M.
AP Manager
United States
We had three applicants in the same week with bank statements that looked completely fine. Two of them were flagged as modified. You simply cannot see this by reading the document — it is in the file structure.
Lars V.
Risk Analyst, Online Lending
Netherlands
Salary slips were coming with altered figures. We identified two problematic files before the placement was finalised.
Priya K.
HR Operations Lead
India
Since we started checking documents this way, we stopped two applications early in the process that would have been very difficult to reverse later.
Julien R.
Fraud Analyst, Fintech
France
Some applicants were sending PDFs that looked authentic but had been edited in ways not visible to the eye. We now ask for checked originals when something is flagged. Already saved us from a few bad decisions.
Marta S.
Compliance Coordinator
Spain
One invoice was caught because there was a mismatch between the document dates and structure. That particular case would have cost us significantly.
Tariq A.
Finance Manager
United Arab Emirates
FAQ
Frequently asked questions
Why deliberately skip OCR?
Is HTPBE? a direct feature replacement for Ocrolus?
How do you compare on price?
When does HTPBE? fit, and when does it not?
What can HTPBE? not tell me about a bank statement?
Can I use HTPBE? and Ocrolus together?
What does the API return for a bank statement?
Can we evaluate before committing?
Secure your workflow
Create your account – check PDFs on the web or with an API key, both ready on signup.
No sales call. Cancel any time.