Risk Graph: How Network Effects Improve Fraud Detection

Traditional fraud detection relies on isolated, company-specific data. TrustRelay Risk Graph aggregates anonymized signals across customers, enabling network-wide pattern recognition and reducing false positives by 60%.

The Limits of Isolated Detection

Every company's fraud detection system operates in a silo. Your company sees only your vendors, your transactions, and your fraud attempts. This creates a fundamental blind spot: you can't detect patterns that span multiple organizations.

Consider this scenario: A fraudster targets five companies in the logistics sector with the same bank account change scheme. Each company sees one suspicious request. No single company has enough data to flag it as a coordinated attack. But if those five signals were aggregated, the pattern would be unmistakable.

How Risk Graph Works

TrustRelay Risk Graph creates a shared intelligence layer across all TrustRelay customers:

Signal Collection

Every verification event generates anonymized signals:

• Bank account validation outcomes
• Vendor identity verification results
• Sanctions screening matches
• Behavioral anomaly indicators

Pattern Recognition

The Risk Graph analyzes signals across the network to identify:

• Shared infrastructure — The same bank account appearing across multiple unrelated vendors
• Temporal patterns — Coordinated change requests targeting multiple companies within a short time window
• Geographic anomalies — Bank accounts in unexpected jurisdictions for the claimed business type

Risk Scoring

Each vendor entity in the graph receives a continuously updated risk score based on:

• Direct verification results from the customer's own checks
• Network signals from the broader TrustRelay customer base
• Historical patterns for the vendor's industry and geography

The Network Effect Advantage

Risk Graph gets smarter with every customer added to the network:

• More coverage — A larger network sees more attack patterns earlier
• Fewer false positives — More data points improve scoring accuracy
• Faster detection — Patterns are identified when they first emerge, not after they've caused losses

In practice, customers connected to Risk Graph see a 60% reduction in false positives compared to isolated detection systems.

Key Takeaways

• Network effects enable earlier detection of fraud patterns across industries
• Anonymized risk signals protect customer privacy while improving detection accuracy
• Risk Graph gets smarter with every customer, creating a defensible moat

Privacy by Design

Risk Graph is built on a privacy-first architecture. All signals are anonymized before entering the shared graph. No customer can see another customer's data. The system aggregates statistical patterns, not raw transaction details.

Learn more about TrustRelay Risk Graph →