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Protecting document integrity across banking operations — from customer onboarding and credit decisioning to fraud investigation and regulatory compliance.
Scenario: An applicant submits a passport scan or driver’s licence image as part of a remote account opening process. The document appears visually legitimate but has been digitally altered or entirely generated using AI tools. What the API detects:
  • AI-generated identity document images created using generative models
  • Digitally altered photographs where the applicant’s photo has been swapped
  • Synthetic document templates that mimic legitimate government-issued formats
  • Inconsistent fonts, spacing, or formatting that indicate post-production editing
  • Metadata anomalies suggesting the document was produced by image editing software
Example response: 
{
  "analysis_id": "df-id-2025-001",
  "is_ai_generated": true,
  "confidence_score": 87.3,
  "risk_level": "critical",
  "detection_details": [
    {
      "type": "PHOTO_MANIPULATION",
      "description": "Facial photograph shows signs of digital replacement",
      "confidence": 91.2
    },
    {
      "type": "FONT_INCONSISTENCY",
      "description": "Character spacing on line 3 deviates from standard document template",
      "confidence": 78.5
    }
  ]
}
Scenario: A loan applicant submits bank statements and pay stubs to demonstrate income and financial stability. The documents have been manipulated to inflate income figures and conceal adverse transaction history. What the API detects:
  • Manipulated transaction amounts or balances within bank statement PDFs
  • Fabricated financial statements generated using document templates or AI
  • Altered dates, account numbers, or institutional branding
  • AI-generated financial documents that do not originate from a legitimate financial institution
  • Pixel-level editing artifacts in scanned or digital documents
Example response: 
{
  "analysis_id": "df-fin-2025-042",
  "is_ai_generated": false,
  "confidence_score": 72.1,
  "risk_level": "high",
  "detection_details": [
    {
      "type": "CONTENT_MANIPULATION",
      "description": "Transaction amounts on page 2 show evidence of digital alteration",
      "confidence": 82.4
    },
    {
      "type": "METADATA_ANOMALY",
      "description": "PDF creation metadata inconsistent with stated issuing institution",
      "confidence": 65.8
    }
  ]
}
Scenario: A recorded phone verification or voice authorization is submitted as part of a high-value transaction approval process. The voice may have been cloned using publicly available AI voice synthesis tools. What the API detects:
  • Synthetic voice generation using text-to-speech or voice cloning models
  • Voice cloning artifacts including unnatural prosody and spectral anomalies
  • Spliced audio segments where different recordings have been combined
  • Background noise inconsistencies suggesting studio or synthetic generation
Example response: 
{
  "analysis_id": "df-audio-2025-018",
  "is_ai_generated": true,
  "confidence_score": 91.6,
  "risk_level": "critical",
  "detection_details": [
    {
      "type": "SYNTHETIC_VOICE",
      "description": "Voice characteristics consistent with AI voice cloning model output",
      "confidence": 93.1
    },
    {
      "type": "SPECTRAL_ANOMALY",
      "description": "Frequency spectrum shows patterns inconsistent with natural human speech",
      "confidence": 88.4
    }
  ]
}
Scenario: Profile images or photographs are submitted as part of identity verification, business documentation, or insurance claims. The images may be AI-generated or digitally manipulated to misrepresent the individual or situation. What the API detects:
  • AI-generated headshots produced by GAN (Generative Adversarial Network) models
  • Manipulated photographs with digitally altered backgrounds, clothing, or context
  • GAN-generated faces that exhibit characteristic symmetry and texture patterns
  • Image splicing where elements from multiple photographs have been combined
Example response: 
{
  "analysis_id": "df-img-2025-007",
  "is_ai_generated": true,
  "confidence_score": 95.2,
  "risk_level": "critical",
  "detection_details": [
    {
      "type": "GAN_GENERATED_FACE",
      "description": "Facial features exhibit patterns characteristic of GAN-generated imagery",
      "confidence": 96.8
    }
  ]
}
All file analyses are logged and available in the Admin Console for audit purposes. Navigate to Settings > Audit Logs to review analysis history, filter by date range, and export records for regulatory reporting.