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IDnow Platform Documentation

website·Indexed 40 days ago

https://docs.eu.platform.idnow.io/

Technical documentation for the IDnow identity verification platform. Includes guides for document-based IDV (v3, v4, and headless), eID authentication, electronic signatures, and eKYC checks. Provides API references for session management, data block retrieval, webhook event structures, and flow-specific OpenAPI specifications.

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What's inside IDnow

  1. Overview of Headless Document Identity Verification (DOC_ID_HEADLESS:v2)

    The DOC_ID_HEADLESS:v2 step allows for identity verification using static evidence (such as document images) collected by the customer's own front end and submitted via API. Unlike Capture mode, Headless mode involves no user interaction within the IDnow front end.

    Key Constraints:

    • Static Evidence Only: It relies solely on static images. Dynamic checks like NFC chip reading, hologram movement inspection, or live liveness actions cannot be performed.
    • Use Case: Best suited for unregulated scenarios (e.g., data extraction, image quality assessment, pre-validation). It is not suitable for regulated KYC or AML processes due to lower accuracy compared to Capture mode.
    • Step Compatibility: This step requires an ID-category document type (passport, driving licence, national ID card). It cannot be combined in the same flow as IBAN_VERIFICATION:v1 or PROOF_OF_ADDRESS:v1.

  2. Overview of Headless Signing

    Headless Signing is a backend-only operation used to sign or seal documents instantly without user interaction. It integrates with the Sphinx credential system to issue either Advanced Electronic Signatures (AES) for natural persons or Qualified Electronic Seals (QSEAL) for legal entities. The process is asynchronous: it initiates a request, polls for completion via a Temporal workflow, and finalizes the operation server-side.

  3. Overview of Biometric Verification v2

    BIOMETRIC_VERIFICATION:v2 provides real-time identity confirmation using facial recognition. It prevents impersonation, spoofing, and deepfakes through liveness detection (confirming physical presence) and face comparison (matching a live capture against an enrolled biometric template). This step requires that the user has previously completed a Biometric Enrolment step.

    Note: BIOMETRIC_VERIFICATION:v2 is currently in preview. For production flows, use BIOMETRIC_VERIFICATION:v1.

  4. Overview of document-based identity verification and electronic signing

    This process combines document-based identity verification (via DocIDV AutoIdent or VideoIdent) with electronic signature capabilities. It verifies a user's identity through document analysis, security checks, face matching, and liveness detection, then issues a signature.

    Two signing modes are supported:

    1. Qualified Electronic Signature (QES): Legally binding signatures under the eIDAS framework, equivalent to handwritten signatures. Requires strict identity verification.
    2. Contract signing mode: Standard electronic signatures on customer-supplied documents without QES requirements.

    Results are returned in a unified Verification data block (v2) and a signed documents package containing the signed files and audit trail.

  5. Overview of IDnow DocIDV identity verification

    The DocIDV service verifies a user's identity by analyzing documents through two primary methods:

    • VideoIdent (Agent-assisted): A trained IDnow agent guides the user through document capture and verifies security features, face matching, and liveness in a live session.
    • AutoIdent (Automated): An AI-driven process that analyzes document authenticity, security features, face matching, and liveness without human intervention.

    The service can optionally cross-check identity data supplied from upstream steps and returns a unified Verification data block detailing the checks performed and the resulting assurance level.

  6. Overview of eID authentication via eIDs v2

    The eIDs v2 platform allows businesses to authenticate users through government-issued eID schemes (e.g., France Identité, Personalausweis) using a unified step. IDnow manages the routing to the appropriate provider backend via OIDC-based authentication. The result of the authentication is returned as a structured Verification data block containing the method used, the authority, cryptographic evidence, and the achieved assurance level.

  7. Overview of Document-Based Identity Verification (DOC_ID:v4)

    DOC_ID:v4 provides end-to-end identity verification using secure identity documents. In Capture mode, the IDnow frontend guides users through capturing document images, performing biometric checks (face matching and liveness detection), and optionally reading electronic document chips via NFC.

    Key features include:

    • Automated visual verification: AI-based analysis of authenticity and security features.
    • Automated NFC verification: Validation of embedded NFC chips.
    • Agent-assisted verification: Expert review for complex or escalated cases.
    • Configurable capture: Customization of document types, biometric samples, and liveness detection per deployment.

    Note: DOC_ID:v4 is currently in preview. For production flows, use DOC_ID:v3.

  8. Overview of trust-based electronic signature flow

    The trust-based electronic signature flow allows users with existing verified AML identity data to sign documents without repeating full identity proofing, provided their device trust assessment is positive.

    Flow Logic:

    1. Start: Collects input data blocks.
    2. Device signals: Analyzes device trustworthiness.
      • Trusted: Proceeds directly to Signing.
      • Suspicious, Not Trusted, or Inconclusive: Routes to 'Verifying ID document (IDC)' for fresh identity evidence.
    3. Signing: If the device was trusted (or the IDC step was successful), the user signs the document.
    4. End: Session is accepted (success) or rejected (identity expired, technical failure, or fraud detected during IDC).

    This adaptive approach ensures compliance with AML and eIDAS requirements while reducing friction for low-risk users.

  9. Overview of DOC_ID_SIGNING:v3

    The DOC_ID_SIGNING:v3 step provides identity verification combined with electronic signatures (QES or standard contract signing). It extends the v2 functionality by adding an explicit cancelled output route, allowing developers to route cancelled sessions to a retry prompt or rejection step instead of the session simply aborting with an ABORTED outcome. Note: This step is currently in preview and the API may change before general availability.

  10. Overview of BASIC_IDENTITY_COMPARISON:v2

    The BASIC_IDENTITY_COMPARISON:v2 step ensures that identity information collected from different workflow steps or documents refers to the same person. It uses named input slots (leftOperand and rightOperand) wired via inputMapping to resolve data flow. The workflow engine validates at publish time that both referenced steps are reachable and produce a BasicIdentity data block.

  11. Overview of Document-Based Identity Verification (v3) Capture Mode

    Capture mode is an end-to-end identity verification process where IDnow provides the frontend to guide users through providing identity evidence. This includes capturing document images, performing biometric checks (face matching and liveness detection), and optionally reading electronic document chips via NFC.

    Unlike the Headless approach (where the customer collects data), Capture mode optimizes data quality and regulatory compliance (eIDAS, AML, GwG, BaFin) by managing the evidence collection process. Verification can be performed via:

    • Automated visual verification: AI-driven analysis of document authenticity and security features.
    • Automated NFC verification: Secure reading of embedded chips in eID documents.
    • Agent-assisted verification: Expert review for complex or escalated cases.

  12. Overview of EKYC_UK:v1 identity verification

    The eKYC UK step verifies a person's identity by cross-referencing their data against UK credit bureau and electoral roll sources (such as Electoral Roll and Credit Lenders). It is a headless, server-side process that requires no end-user interaction. The step consumes BasicIdentity and ExtendedIdentity data blocks and returns a tiered corroboration result based on the number of independent sources that successfully match the identity elements.