Identity and document verification that drives growth

Screen customers against thousands of sanctions and PEP watchlists including OFAC, UN, HMT, EU, DFAT, and more globally, with real-time updates.

Scan more than 150 million adverse media sources to identify negative news for predicate offenses, and potential risks.

Continuously monitor watchlists and adverse media for changes that may create risk.

Veriff uses machine-learning technology to assess client sessions in real time and guide users during the selfie and document capture. Clear and actionable user feedback due to poor lighting, glare, cropped ID, and obstructed facial images are just a few examples of how Assisted Image Capture pre-validates and boosts first-time pass rates.

In addition to document and selfie checks, Veriff automatically checks more than 1,000 data points such as network and device analytics to combat evolving fraud typologies.

Veriff can determine if a session or user is real or a spoof attempt, without unnecessary input from the user. Your customers won’t have to complete any additional actions for Veriff’s liveness detection.

Biometric information is extracted from photos and an optional non-disruptive background video to confirm that a user is who they say they are.

Our identity verification experts work with you to determine which data inputs Veriff should gather from your users, as well as what checks to conduct depending on your conversion and compliance needs. End-user flows can be as simple as an automated ID check, or as advanced as a fully recorded and manually reviewed session, including selfies and a photo with their chosen ID.

You can modify the color schemes in Veriff’s mobile and web end-user flows to align with your branding, as well as add your logo. From color customization to complete white labeling, our team is here to help you seamlessly integrate into your customer journey.

Our verification flows come with optional background video recording, allowing us to conduct advanced fraud prevention checks with minimal input from your users.

Reinforced learning from human feedback (RLHF) means that newly issued document specimens can rapidly be added to our database and be used within our automation without risking high false positive rates due to untrained algorithms.

Veriff uses machine learning to compare the asserted identity document with our specimen document database. This process can identify fraudulent and tampered documents.

Our extensive document database improves document detection and identification. Data is automatically extracted from the asserted identity document as soon as it is submitted which speeds up verification sessions. Using Webhooks, you can instantly receive primary data, such as name, expiration date, birth date, and document number depending on your requirements.