Approve clean files automatically
Let low-risk uploads continue into OCR, KYC, underwriting, AP, or agent workflows without adding manual review to every case.
Use DocVerify's document fraud detection API to flag edited receipts, forged PDFs, fake statements, manipulated IDs, and payment screenshots before OCR, approval logic, or AI agents act on them.
{
"risk": "high",
"decision": "escalate",
"fraud_signals": [
"recompressed_region",
"metadata_anomaly",
"synthetic_pattern"
],
"recommended_action": "manual_review"
}curl -X POST https://docverify.app/api/analyze \ -H "X-API-Key: sk_live_..." \ -F "file=@receipt.pdf" \ -F "include_heatmap=true"
Let low-risk uploads continue into OCR, KYC, underwriting, AP, or agent workflows without adding manual review to every case.
Route likely edits, synthetic regions, metadata anomalies, and recompression signals to an analyst queue with evidence attached.
Stop payouts, account approvals, reimbursements, shipment releases, or agent tool calls before a forged document becomes trusted data.
Commercial use cases
The API is built for teams that already receive user-submitted documents and need a machine-readable fraud signal before downstream systems approve, reimburse, onboard, or release value.
Screen Zelle, Cash App, Venmo, and other proof-of-payment screenshots before a seller ships goods or marks an order paid.
Verify receipts, invoice PDFs, and ERP audit-trail attachments before OCR or an AI expense agent turns them into approved spend.
Check IDs, bank statements, utility bills, payslips, and proof-of-address uploads before a customer, merchant, or borrower is trusted.
API placement
Fraud detection is most valuable at file intake. The workflow stays simple: upload, analyze authenticity, branch on risk, then let OCR and automation touch only the documents that pass policy.
upload_received
-> document_fraud_detection_api
-> risk: low -> continue OCR / automation
-> risk: medium -> request extra evidence
-> risk: high -> manual review / block actionA document fraud detection API lets a product submit an uploaded document and receive a structured fraud-risk assessment. DocVerify checks forensic signals such as compression artifacts, rendering inconsistencies, metadata traces, synthetic image patterns, and suspicious edited regions before a workflow trusts the file.
Document verification is the broader trust workflow around an uploaded file. Document fraud detection is the risk-scoring layer inside that workflow: it focuses on whether the document appears forged, manipulated, AI-generated, or inconsistent with an authentic source file.
Run it immediately after upload and before OCR, rule engines, human approval queues, KYC checks, underwriting decisions, AP reimbursement, or AI agents use extracted document data.
Common candidates include receipts, invoice PDFs, bank statements, pay stubs, IDs, proof-of-address files, payment screenshots, insurance documents, and onboarding evidence.
Start with the API docs, or compare the broader document verification API surface for onboarding, AP, underwriting, and agent workflows.
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