How an Insurance Company Raised Face Authentication Success from 78% to 96% in Its Mobile App

A major insurance company from Eastern Europe faced an unexpected challenge while digitizing their claims submission and policy issuance process through a mobile app used by over 500,000 active customers.

Their app featured a robust identity verification flow, including document upload, selfie-based face matching and liveness checks.

On paper, everything worked. But shortly after launching facial authentication functionality, their support team began receiving up to 180–200 daily complaints about verification failures and access denials.

A deep-dive analysis revealed a key issue: 22% of users were failing the photo verification. That meant nearly one in five customers couldn’t complete their identity checks and purchase insurance products.

Here’s what was going wrong:

• Poor capture conditions: Users often took photos in motion — inside cars, building lobbies, or outdoors in poor lighting with unstable internet connections. This led to low-quality facial images.

• No feedback loop: The system didn't guide users on how to take better photos or explain why their submission was rejected.

These weren’t fraud attempts — they were real customers trying to complete the remote identification. Yet the used system wasn’t built to handle such real-world cases.

In short, the integrated facial authentication solution lacked flexibility and failed under typical conditions of everyday mobile usage.

To fix this, the insurer switched to 3DiVi BAF (Biometric Anti-Fraud) — a more adaptive facial authentication system for online identity verification scenarios.

What makes 3DiVi BAF different?

• Built-in quality checks aligned with international standards and Zero Trust principles.

• One-touch verification: Just 4 seconds and 5 foundational checks per session.

• Cross-platform compatibility: A single API for both mobile and desktop apps.

• Full customization: The company could define its own confidence thresholds, verification logic, and adaptive rules.

• Smarter face matching: A trained model optimized for low-quality or unconventional photo inputs.

• Liveness detection: Protection against photo, mask, and injection spoofing attacks.

• Real-time photo quality feedback: Helping users correct issues on the spot.

The insurer embedded 3DiVi BAF into the mobile app and ran real-world tests. They also added in-app tips to help users improve their photos and provide instant feedback when verification failed.

The impact was immediate and impressive:

• Face verification success rate jumped from 78% to 96%

• Application processing time improved by 23%

• Support requests dropped significantly, as users rarely encountered verification issues

Digital transformation isn’t just about sleek web interfaces — it’s about resilience and flexibility in real-world scenarios.

By switching to 3DiVi BAF, the insurer not only cut verification failures and improved customer experience but also built a secure, scalable, and user-friendly biometric system ready for future growth.

Struggling with failed photo verifications in your app? Let’s talk—see how adaptive face authentication can reduce drop-offs, cut support costs, and help more real users complete your remote identification scenarios.