Looking for a Resistant AI alternative? — Self-serve PDF fraud detection without the enterprise minimum
Risk-ops and fraud teams know Resistant AI as an established document and transaction fraud platform serving large enterprises. htpbe? solves the document-fraud half of the problem — at the file layer — and ships it with public pricing, free test keys, and no enterprise minimum. If you're a smaller fintech, mortgage operator, or proptech platform without enterprise budget, we're positioned for you.
htpbe? analyzes the structural layer of the PDF file — producer, xref, metadata, image streams, signature chain, balance arithmetic. We don't cover transaction fraud, behavioural analytics, or AML monitoring. Resistant AI has broader feature scope across document and transaction fraud; this page describes how htpbe? fits the document-fraud structural layer specifically, for teams without enterprise budget.
One REST call, one deterministic verdict
Upload the PDF. The API returns INTACT, MODIFIED, or INCONCLUSIVE with named markers — in about three seconds.
Why teams without enterprise budget pick htpbe?
Three real fraud mechanics we catch at the structural PDF layer.
No enterprise minimum
Plans from $15/mo (30 requests) to $499/mo (1,500 requests) with public pricing. Enterprise (unlimited, on-premise) exists for teams that need it, but the smaller plans are real plans — not gated trials. Smaller fintechs, proptech, and lender risk teams can ship htpbe? without committing to enterprise contracts.
Self-serve developer experience
Sign up, get an API key, ship the integration the same day. No sales call, no procurement process. Test keys included free on every plan — your engineers prototype against deterministic test fixtures before any commercial decision.
Transparent forensics, not a black box
htpbe? returns INTACT, MODIFIED, or INCONCLUSIVE with named markers (xref count, producer signature, signature chain, balance arithmetic, image-stream artefacts). Your underwriting or risk logic interprets the markers in context — we don't hide the forensics behind a single risk score or a black-box AI verdict.
How htpbe? is positioned
When htpbe? makes sense (and when Resistant AI might fit you better)
Smaller team, document-fraud focus? htpbe? Enterprise scope across docs + transactions? Look at both.
Honest sizing: pick the tool whose scope and price match yours.
htpbe? is built for risk-ops and fraud teams that want clean structural document forensics at a transparent price, without enterprise minimums. If your team needs document-fraud detection bundled with transaction-fraud monitoring, behavioural analytics, AML monitoring, and a managed enterprise relationship, Resistant AI and similar enterprise platforms have that scope — that's a fair reason to evaluate them. If you have document-fraud as a focused need and want a primitive your team integrates without enterprise commitment, htpbe? is positioned for that. Both serve real fraud-ops needs; the right answer depends on whether your scope is broad or focused.
Five forensic layers, one deterministic verdict
Every PDF we receive passes through the same structural pipeline — no model training, no thresholds to tune.
Metadata analysis
Creation and modification timestamps, producer and creator fields, XMP metadata — the first layer exposes basic tampering.
File structure
Xref tables, trailer chain, incremental updates. Any edit after export leaves a structural fingerprint here.
Digital signatures
Signature chain integrity and post-signature modifications produce deterministic markers. Certainty-level signal.
Content integrity
Fonts, objects, embedded content, page assembly. Multi-session edits and inserted objects are visible at this layer.
Verdict with markers
Deterministic output: INTACT / MODIFIED / INCONCLUSIVE, with named markers for every finding — suitable for audit trail.
PDFs we analyze for risk-ops and fraud teams
Every type listed below is analyzed at the structural file layer — not the rendered image.
Detection capabilities
Deterministic structural signals. No probabilistic scores, no model training.
Producer signature analysis
Authentic documents come from institutional sources — banking systems, payroll engines, accounting software, government issuance. When the producer field shows a desktop tool or generator-tool fingerprint, htpbe? flags it. The verdict is interpretable, not a black-box score.
Incremental update detection
Edits to a real PDF leave incremental update markers in the xref chain. htpbe? flags these as MODIFIED at high confidence — visible structural evidence that the file was edited after issuance.
Balance arithmetic verification
Running balance is verified row-by-row across bank statements. Edited transactions break the chain unless every dependent balance was also adjusted — a structural fraud signal independent of OCR.
Digital signature chain validation
Tax forms, employer letters, government PDFs, and many institutional documents carry digital signature chains. htpbe? validates the chain and flags invalidated or removed signatures as MODIFIED at certain confidence.
Image-stream artefact detection
Lifted-and-pasted logos, signatures, and headers leave compression and object-structure artefacts that differ from authentic embedded content. The image-stream metadata exposes paste operations.
Cross-document fingerprint analysis
When multiple "different" documents from a single applicant pool share font subset prefixes, image hashes, or producer signatures, the API surfaces the shared fingerprints — useful for catching synthetic-identity rings.
A Resistant AI alternative your engineers can ship today
Buyers can skip this section — developers, the integration is two HTTP calls.
Step 1 — submit the PDF
curl -X POST https://api.htpbe.tech/v1/analyze \
-H "Authorization: Bearer $HTPBE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"url": "https://your-storage/applicant-bank-statement.pdf"}'Step 2 — read the verdict
{
"id": "r1e2s3i4-5s6t-7a8n-9z0t-a1b2c3d4e5f6",
"status": "modified",
"modification_confidence": "high",
"modification_markers": [
"Two cross-reference tables — incremental update",
"Modification date 11 days after creation date",
"PDF editor producer detected"
],
"producer": "Adobe Acrobat Pro",
"creator": "Bank of America Online",
"creation_date": 1707091200,
"modification_date": 1708041600,
"has_digital_signature": false,
"xref_count": 2,
"has_incremental_updates": true
}Original came from Bank of America Online — institutional source. 11 days later it was opened in Adobe Acrobat Pro and re-saved, adding a second xref. Verdict: modified at high confidence with named markers — your underwriting logic decides what to do with the verdict.
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 verified 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
Frequently asked questions
Related solutions and guides
Fintech & Lending
Full lender vertical positioning — fraud-ops angle for risk teams.
Insurance Claims
Document fraud detection for claims-ops and SIU teams.
Bank Statement Fraud Detection
Bank statement structural forensics — the most common document-fraud target.
Secure your workflow
Create your account — API key on signup, free test environment on every plan.
From $15/mo. No sales call. Cancel any time.