Verification checks
To prove the identity of your customers, Transmit performs various checks that depend on the verification flow your app implements. Once a verification process is completed, the verification result will include a recommendation on how to proceed. For example, the diagram below represents the checks performed during the document verification process.
Document check
Verifying a government-issued identity document is used to establish the real-world identity of the user. It consists of:
Document scanning
The process begins by scanning a government-issued identity document, such as a driver's license or passport. Using their mobile device, the user takes a picture of the front and back of the document. These images are analyzed in real-time to check for potential issues that may cause them to be rejected and the user is instructed on what to do next. For example, the user is notified if:
- image is blurry
- document isn't supported
- document wasn't captured
- image is too dark
- info is missing or isn't visible
- glare is detected
Document classification
Once scanned, documents are automatically classified. We support over 10,000 documents worldwide (see Full Global Coverage). If a scanned document cannot be classified, we perform a manual review process that checks if a new document type was released and if so, we train our models to support it.
For example, documents are classified by:
- country
- type (Driver License, Passport, National ID, etc.)
- document template
Document authentication
The authenticity of the document is validated to make sure it hasn't been altered or forged. This is done by checking:
- security features, such as holograms and laser engravings
- portrait photo issues or inconsistencies
- cross-references of personal and document data throughout the document
- mismatches between the template used and the issue date
- correctly formatted dates (e.g., MM/DD/YYYY vs DD/MM/YYYY)
- correct fonts, including non-generic ones that can't easily be matched
- check digits or checksums in the machine-readable data (MRZ lines)
Selfie & liveness check
In face authentication scenarios, a selfie check is used to ensure that the user indeed interacts with the app and is who they claim they are. In the document verification flows, a selfie check acts as a second layer of verification—if the uploaded document proves to be authentic, a check will ensure that it wasn't stolen. The selfie check consists of:
Face liveness
Before a selfie of the user is matched to the photo ID in the document or the reference image, the selfie is analyzed to validate that it's indeed a live person. For example, liveness detection is used to detect:
- printed photos
- video replays
- high-end silicone masks
- photos on screen
- face cutouts
- realistic mannequins
Face matching
To complete the selfie check, the user's selfie must match the photo ID in the document or the reference image. Our face comparison technology was trained on over 400,000 faces and has been proven reliable, taking into account factors like:
- age-match accuracy
- race
- gender
- black & white vs color photos
Fraud detection
Our hosted identity verification solution includes a built-in integration with our Detection and Response services to add an additional layer of detection to the verification process. In addition to the verification checks, risk and trust are continuously assessed throughout the process. For each user interaction, telemetry data is streamed to the platform. Machine learning models and heuristics are applied to this data to detect risk signals, such as bots, spoofed devices, behavioral anomalies, malicious networks, and more.