Analyze a file for fraud detection using the specified model.
Available Models:
document - For PDFs and document images (bank statements, invoices, IDs)object - For general images and AI-generated content detectionLikelihood: Returned as 0-100% where higher values indicate higher probability of manipulation/fraud.
Tags Parameter: Pass optional metadata as a JSON object to organize and filter your analysis results.
Recommended tag fields:
| Field | Description | Example |
|---|---|---|
customerId | Your customer’s unique identifier | "9999" |
customerName | Customer or account name | "Acme Corp" |
documentId | Your internal document reference | "DOC-2024-001" |
companyName | Company name | "DeepXL" |
companyId | Company identifier | "COMP-001" |
Example tags value: {"customerId":"9999","customerName":"Acme Corp","documentId":"DOC-2024-001","companyName":"DeepXL","companyId":"COMP-001"}
You can filter results by tags using the GET endpoint: ?tagFilter=customerId=9999
Bearer authentication header of the form Bearer <token>, where <token> is your auth token.
Model to use for analysis; Available models: document, object
Optional JSON object with key-value pairs for organizing and querying results. Example: {"customerId":"9999","customerName":"Acme Corp","documentId":"DOC-2024-001","companyName":"DeepXL","companyId":"COMP-001"}. Common keys: customerId, customerName, documentId, companyName, companyId.
Analysis completed successfully
The fraud detection result