Beyond Rules and Attributes
Traditional fraud detection relies on rules and isolated entity analysis. It catches known patterns but misses the sophisticated schemes that exploit network relationships. Fraud is a network problem. A company might look clean in isolation, but its connections reveal the truth: shared addresses with known bad actors, ownership links to shell entities, suppliers with suspicious patterns.Our Graph Foundation Model analyzes entities within their relationship context—capturing multi-hop patterns that traditional approaches can’t see.
How Avra Detects Fraud
1
Entity Intelligence
Registration anomalies, address inconsistencies, ownership patterns, activity mismatches
2
Network Analysis
Multi-hop relationships: counterparties, suppliers, ownership chains, shared infrastructure
3
Behavioral Patterns
Transaction velocity, payment patterns, seasonal variations—compared against similar legitimate entities
4
Temporal Dynamics
How is this entity evolving? Rapid changes in network position often signal fraud or distress
What We Catch
Use Cases
Onboarding Verification
Screen new counterparties before establishing relationships
Transaction Monitoring
Real-time risk signals for payment authorization
Portfolio Screening
Periodic review of existing relationships for emerging risks
Investigation Support
Deep network analysis when suspicious activity is detected
Powered by two foundations
Fraud detection is where Avra’s network intelligence is most direct. The Graph Foundation Model has learned what legitimate and suspicious network structures look like across the broader economy. Your Relational Foundation Model has learned the shape of your own transactions, customers, and accounts. The downstream fraud model is trained on top of both — and every training run feeds signal back into your RFM, making the next iteration sharper.Customer Data Needed
Output Schema
Evaluation Metrics
- PR-AUC — Primary metric, given the rarity of fraud events. Measures precision-recall trade-off across all thresholds.
- ROC-AUC — Overall discrimination between fraud and legitimate activity.
- False Positive Rate at fixed recall — Operational metric: how many legitimate entities are flagged at your desired catch rate.
Multi-resolution embeddings for fraud
Avra’s adaptive embeddings enable multi-resolution fraud architectures:- Transaction-level (64–128d) — lightweight embeddings for real-time transaction scoring where latency is critical. Fast enough to run on every payment.
- Entity-level (512–1024d) — full-resolution embeddings for deep entity analysis. Rich context about the entity including network position, ownership patterns, and behavioral history.