Document Fraud Detection

Document Fraud Detection uses AI vision models to analyze uploaded identity documents for signs of tampering, forgery, and manipulation. Every document uploaded to Verilock is automatically scanned for fraud signals.

How It Works

Document upload

The user uploads an identity document (passport, ID card, driver’s license) through the hosted flow or API.

AI analysis

The AI engine analyzes the document image for visual inconsistencies, font anomalies, edge tampering, and metadata manipulation.

Fraud scoring

The analysis produces a fraud score (0-100), a recommendation, and a list of specific fraud signals detected.

Decision

The fraud score feeds into the session’s overall risk score and can trigger orchestration rules for automatic decisions.

Fraud Signals

Verilock detects the following fraud indicators:

Signal	Description
`font_mismatch`	Text uses inconsistent fonts compared to known genuine templates
`edge_tampering`	Visible cut-and-paste artifacts or irregular edges around text or photo
`photo_overlay`	Selfie photo appears to be digitally overlaid onto the document
`metadata_mismatch`	Image EXIF data inconsistent with document (e.g., edited in Photoshop)
`resolution_anomaly`	Inconsistent resolution across different areas of the document
`micro_print_absent`	Expected security micro-printing not detected
`hologram_absent`	Expected holographic elements not visible
`color_profile_anomaly`	Color saturation or gradient inconsistencies versus genuine templates
`barcode_mismatch`	Machine-readable zone (MRZ) or barcode data conflicts with visual text
`screen_capture`	Document appears to be a photo of a screen rather than a physical document

Fraud Score

Score Range	Level	Recommendation
0 — 15	Low	`accept` — no fraud indicators detected
16 — 40	Medium	`review` — minor anomalies worth manual inspection
41 — 70	High	`review` — multiple fraud signals present
71 — 100	Critical	`reject` — strong evidence of fraud or tampering

API Response

Fraud detection data is included in the session detail response (GET /v1/sessions/{id}):

{
  "id": "ses_a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "status": "declined",
  "document": {
    "type": "passport",
    "issuing_country": "GB",
    "fraud_detection": {
      "fraud_score": 78,
      "fraud_level": "critical",
      "fraud_recommendation": "reject",
      "fraud_signals": [
        {
          "signal": "font_mismatch",
          "confidence": 0.92,
          "description": "Surname field uses a different font weight than expected for this document type"
        },
        {
          "signal": "edge_tampering",
          "confidence": 0.87,
          "description": "Irregular edges detected around the photo area"
        },
        {
          "signal": "metadata_mismatch",
          "confidence": 0.95,
          "description": "EXIF data indicates image was last modified in an editing application"
        }
      ],
      "analyzed_at": "2026-03-18T10:00:00Z"
    }
  }
}

Clean Document Example

{
  "document": {
    "type": "id_card",
    "issuing_country": "DE",
    "fraud_detection": {
      "fraud_score": 5,
      "fraud_level": "low",
      "fraud_recommendation": "accept",
      "fraud_signals": [],
      "analyzed_at": "2026-03-18T10:00:00Z"
    }
  }
}

Response Fields

Field	Type	Description
`fraud_score`	integer	Document fraud score (0-100)
`fraud_level`	string	`low`, `medium`, `high`, or `critical`
`fraud_recommendation`	string	`accept`, `review`, or `reject`
`fraud_signals`	array	List of detected fraud indicators
`fraud_signals[].signal`	string	Signal identifier
`fraud_signals[].confidence`	number	Detection confidence (0-1)
`fraud_signals[].description`	string	Human-readable description of the finding
`analyzed_at`	string	ISO 8601 timestamp of analysis

Security Features

Template matching — documents are compared against a library of genuine templates for 190+ countries
Cross-field validation — MRZ data is validated against visual OCR for consistency
Recapture detection — detects photos of screens, printouts, or photocopies
EXIF analysis — image metadata is checked for editing software signatures

Fraud detection runs automatically on all document uploads. No additional configuration is required.

Combine document fraud detection with orchestration rules to auto-decline sessions with critical fraud scores, or route medium-risk documents to your compliance team for manual review.

Getting Started

Integrations

Reference

AML Screening

Transaction Monitoring

Verification Enhancements

Data & Compliance

Decentralized Identity

Document Fraud Detection

How It Works

Fraud Signals

Fraud Score

API Response

Clean Document Example

Response Fields

Security Features

​How It Works

​Fraud Signals

​Fraud Score

​API Response

​Clean Document Example

​Response Fields

​Security Features

How It Works

Fraud Signals

Fraud Score

API Response

Clean Document Example

Response Fields

Security Features