Trusted digital identity powered by biometrics

A biometric platform for identity verification and fraud prevention in digital identity systems

3DiVi BAF

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Secure onboarding, identity proofing and authentication with enterprise-grade omnichannel biometrics

Aligned with NIST SP 800-63 Digital Identity Guidelines

Protect the digital identity ecosystem through passive liveness, deepfake detection, and identity risk management

Fraud passes. Legitimate users fail. Risk teams see outcomes — but not causes.

Metrics show what happened. They do not explain why.

Modern identity verification platforms rely on fragmented signals, aggregated metrics and implicit decision logic.

PROBLEM

Digital identity systems create the illusion of control

user drop-off during identity verification

verification attempts classified as fraud

40-65%

4 in 100

Traditional identity stack

The platform goes beyond “pass” or “fail,” revealing failure causes, user friction points, and attack entry stages.

3DiVi BAF introduces a centralized decision layer that connects signals, interpretation and risk policy into a controllable digital identity system.

3DiVi BAF APPROACH

From fragmented signals to explicit identity decisions

3DiVi BAF architecture

Designed for high-trust digital services

Banking

Reduce identity fraud and protect customer onboarding.
Remote services

Verify users remotely across telecom, insurance and platforms.
Government

Enable trusted digital identity for citizen services.

Operational control

Biometric signal engine

Security & infrastructure

WHY 3DiVi BAF

Threat visibility

Observe identity failures, attacks and anomalies at the session level - not just through metrics.
Identity risk management

Understand why users fail, where fraud succeeds, and how risk evolves across sessions.
Cross-platform authentication

One policy layer across web, mobile and embedded environments
Cost intelligence

Identity verification costs are driven by fraud losses, false rejects and manual review - not infrastructure alone.

High-confidence biometric signals for policy-driven identity decisions

Face verification (1:1)

Verify who someone claims to be with world-class accuracy

Accuracy: 99.9977%
FNMR = 0.0023%
@ FMR = 0.000001 (Visa)
Face identification (1:N)

Identify unknown individuals across your entire database

Accuracy: 99.999%
FNIR @ Rank-1 = 0.0010%
on 12M gallery (Mugshot)
Liveness detection

Generate interpretable anti-spoofing signals across presentation attack scenarios.

Accuracy: 95-99%
APCER = 2% @ BPCER = 1%
APCER < 1% @ BPCER = 5%
Deepfake detection

Detect synthetic identity manipulation and AI-generated biometric attacks.

Accuracy: 95-99%
5% APCER @ 1% BPCER
1% APCER @ 5% BPCER

Zero trust

Replay-resistant session architecture for biometric identity verification.
Privacy-first architecture

100% data-sovereign, customer-run software with fully isolated on-prem deployments and offline license updates.
Web, Android and iOS support

Easy embedding in browsers and desktop/mobile apps.
Image capture component

Omnichannel capture layer
Centralized dashboard

Identity observability layer
Scalability

Scale biometric verification without centralized bottlenecks.

Discuss your project View the docs

3-layer identity decision architecture

1. Signal layer

Biometric and device signals
are normalized into a structured identity context.
2. Interpretation layer

Signals are classified as control, risk, or attack to prevent incorrect system reactions.

Example:
Low image quality ≠ fraud attack
3. Decision layer

Identity decisions are driven
by explicit risk policy — not hidden vendor logic.

Basic decision flow:
Approve • Retry • Step-up • Reject

Session intelligence

● False rejects and UX friction causes

● Drop-off analysis across real sessions

● Image quality and environment failures

● Attack patterns and replay scenarios

Go beyond aggregate metrics to understand real identity sessions.

Explore the concept

View real session insights

Case 1

Case 2

CASE STUDIES

Result:

Key insight:

At launch, nearly 1 in 3 legitimate users failed verification.

But instead of a smooth process, they hit friction everywhere: unclear instructions, confusing interface steps, and repeated rejections.

Challenge: Imagine thousands of people — many of them elderly — trying to pass remote identity verification to receive public subsidies.

The pass rate jumped to 92%, without weakening security requirements.

Solution: 3DiVi BAF analyzed real verification sessions, step by step, and uncovered where users were getting stuck. Not assumptions — actual behavior. Based on this, interaction flows and instructions were refined to match how users actually use the system.

The success of biometric authorization depends heavily on how systems interact with users.

Reducing false rejects in public digital identity systems

digital verification simple for elderly users

RAISING VERIFICATION PASS RATE

Securing kiosk verification from face spoofing

Result:

Key insight:

Challenge: Fraudsters often attempt verification using a phone displaying someone else’s face — a simple spoof requiring little preparation and easy to scale.

Security teams gained clear visibility into the attack pattern, reduced repeat attempts, and made the attack economically unattractive to scale.

Solution: 3DiVi BAF captured and analyzed each attempt in detail, revealing how the spoofing was executed and what made it succeed or fail.

Face anti-spoofing goes beyond attack detection, restricting attacker behavior and strategy.

Securing kiosk verification from face spoofing

FIGHTING FRAUD AT SCALE

Featured resources

Threat model for remote biometric identification in digital banking

In-depth report on biometric threats and defenses against identity fraud (deepfakes, 3D masks, etc.).

Digital identity metrics trap: why banks are losing clients chasing zero fraud

Fraud-only KPIs cost banks revenue and customers. See how NIST metrics guide digital identity and how biometric systems put them into practice.

5 pillars to build a strong
face authentication system

Best practices for implementing face recognition in 2FA/MFA for banking and fintech services.

Digital Identity Paradox: You Verify a Representation, Never a Person

Digital identity is probabilistic, not deterministic concept — and that totally changes how 2FA/MFA systems are built, integrated, and monitored.

Continuous digital identity economics

3DiVi BAF is built for continuous verification at scale — not per-session monetization. Expand your license and scale smoothly, from startup to enterprise grade.

Scalability

Continuous identity protection is now economically viable. Pay only for what you use — no per-request surprises.

Under $0.24 per annual user

3DiVi BAF pricing calculator

3DiVi BAF Pricing Calculator

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Enter the business case and number of users to get an instant estimate.

Business Case

Number of Users

Estimated cost:

$28,800 per year

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Reduce deployment risk before enterprise rollout

Licenses to start

Commercial licenses for development, integration, and pilots — as many as you need
Cost credit

Get 100% of Developer Pack cost back when you upgrade to a commercial license
Priority support

Direct, fast connection to the product team for consultations

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Key questions on controllable digital identity

Build secure digital identity with 3DiVi BAF

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