Introducing Defend's New Fraud Model
Proof has launched Defend, a next-generation machine learning fraud model designed specifically to protect the critical authorization moment in digital transactions by evaluating real-time data—including deepfake detection, behavioral anomalies, and consortium intelligence—drawing on over a decade of identity and transaction data to prevent identity and impersonation fraud with explainable, auditable decisions.
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Proof launches portable digital identity for banks
Introducing portable identity
Updated July 17, 2026
In today’s environment, the moment of authorization is the most consequential, not just another data point. This is where Proof’s next-generation machine learning fraud model, comes into play. Built to protect the moment when decisions are made, agreements are signed, or funds are moved, this new fraud model is the intelligence behind Defend, our enterprise-grade solution for preventing identity and impersonation fraud across any digital interaction.
Key takeaways
- Most fraud models are tuned for onboarding. Authorization-stage fraud, the moment a wire moves, a document is signed, or an account is changed, requires a dedicated fraud model with different signals and different precision requirements.
- Defend evaluates transactions in real time before authorization executes, issuing an approve, deny, or hold decision with an explainable rationale.
- The model draws on 10+ years of identity and transaction data, 600,000+ hours of session recordings, 3M+ annual transactions, and Visa Protect Risk Insights to detect patterns that single-signal models cannot see.
- Deepfake detection, behavioral anomaly analysis, and cross-network consortium intelligence are built into the model, addressing the AI-powered fraud threats that static rule-based systems miss.
- Every decision is accompanied by contributing signals and rationale, making Defend auditable and usable by compliance, investigations, and risk teams.
- As more institutions adopt Defend, each organization contributes fraud signal data back to the network, strengthening detection for all participants over time.
Defend Improves Your Enterprise Risk & Fraud Prevention Strategy
Most fraud and risk-scoring models are tuned for early stages like onboarding, account opening, applications, etc. Defend by contrast, is purpose-built to evaluate transactions in real-time, at the critical juncture when high cost interactions, high value transactions, or sensitive operations are finalized.
From an enterprise perspective higher stakes require higher precision. The cost of incorrectly blocking a legitimate customer negatively impacts the customer’s experience, company revenue, and ultimately trust; and failing to prevent fraud can result in huge losses. Enterprises also operate across multiple silos and platforms, which is why Defend draws from ID verification, transaction, video, behavioral, and consortium data to give a holistic view of transactions in their own context — a view that single-signal models lack. And with widely available generative AI and fraud-as-a-service tools, fraud is evolving fast. Which is why Defend is designed for constant refinement using real-world signals and human feedback loops instead of static rules and weights.
Defend At a Glance (Training, Risk Signals, and Differentiators)
Training that’s built to outperform legacy solutions:
- 10+ years of historical identity verification and transaction data, totaling more than $600B in high-value flows across verticals like real estate, wealth, retirement, and more.
- 600,000+ hours of high-fidelity session recordings captured on the Proof platform, and enriched by trusted agents, enabling robust deepfake detection and behavior analysis.
- Millions of enrolled and verified identities, and 3M+ transactions per year enrich risk decisions with cross-network likeness comparisons and cross-entity transaction histories.
- Proof leverages Visa Protect Risk Insights to train our risk models and compare fraud patterns across two massive networks — Proof’s platform, and Visa’s payment intelligence infrastructure.
- Thousands of trusted agents on the Proof platform continually report suspicious users and activity across millions of identity verifications, online notarizations, and other transactions for continuous real-world model refinement.
- From device fingerprinting, account age, IP/injection anomalies, data mismatches, to user behavior features—OmniTrust synthesizes hundreds of diverse risk signals.
Differentiators v. Other Risk Models
| Dimension | Defend | Other Risk Models |
|---|---|---|
| Core focus | Protect organizations from AI-powered fraud, and increase authorization rates for legitimate customers | Minimize financial losses |
| Deepfake and spoof detection | Real-time monitoring of visual likeness and behavioral anomalies | Usually limited to post-event or passive evaluation |
| Transaction context | Understands context (e.g. vacant land, funding, sensitivity) in risk scoring | Usually optimized for volume-based “average” transactions |
| Network and consortium intelligence | Merges Proof + Visa network intelligence for broader training and fraud pattern visibility | Usually confined to internal or vendor data |
| Feedback loop and model evaluation | Continuous iteration and refinement based on trusted human feedback | Periodic batch retraining, less responsive to emerging threats |
The Advantages of Using Defend
Unprecedented protection against AI-powered fraud
With Proof's deepfake detection trained on video data and the integration of Visa's global fraud intelligence, you can stop sophisticated, high-stakes fraud that traditional, single-source models simply cannot see.
Increased authorization rates for legitimate customers
Defend's comprehensive score results in fewer false positives. Legitimate customers are frictionlessly routed through your system, reducing drop-off and improving conversion.
Scale with confidence & compliance
Defend is built to meet enterprise demands. It’s scalable, explainable, and auditable; and its decision rationale can feed into compliance and audit workflows, helping maintain oversight and traceability.
Shared learning across enterprise ecosystems
As more institutions adopt Defend, each organization contributes signal and fraud pattern data — strengthening the model’s defenses over time. This network effect is rare in siloed risk infrastructures.
How Defend Fits into Your Enterprise Risk Architecture
- 1.Ingestion & enrichment. Defend aggregates real-time transaction, identity, behavior, and session signals.
- 2.Scoring & decisionmaking. The Defend fraud model evaluates transactions and requests, before they’re authorized, issuing a risk score and a decision (approve, deny, hold).
- 3.Explainability & auditability. Each decision is accompanied by its contributing signals, rationale, and data, that can be surfaced to compliance, investigations, and risk teams.
- 4.Model refinement. Outcomes (fraud, false positive, escalation) feed back into the training pipeline—continuously improving detection capabilities.
Real-world use-cases include:
- High-value agreements or contracts like real estate closings, securities agreements, and retirement plan changes.
- Fund transfers or payouts like wire instructions, large ACH movements, and fund disbursements.
- Sensitive account updates like changing ownership, authorizing new users, or updating beneficiary information.
- Regulated identity workflows like online notarization, remote identity proofing, and KYC operations.
Proof Has a Proven Track Record in Risk Scoring and Fraud Detection
We have already safeguarded over $600B+ in digital transactions across the globe. With Defend, you’ll gain:
- The broadest signal coverage in the industry
- Deep expertise in identity verification, deepfake, and fraud detection
- An extensible decisioning engine designed for enterprise-grade scale, compliance, and governance
Together, we’ll help shift your fraud prevention operations from reactive and static to proactive and adaptive.
Want to Learn More About How Defend is Securing the Future of Digital Commerce?
Explore our capabilities in depth at Proof.com/capabilities/omnitrust or engage our sales and solution engineering team directly.
Let’s redefine what it means to trust a transaction—in real time, at scale.
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