Stop Financial Crime
Before It Impacts
GraphMule analyzes complex transaction networks in real-time to uncover cycles, smurfing, and shell accounts that traditional rules miss.
Suspicious Activity Detected
Critical AlertAnomalous layering pattern detected across 12 linked accounts with high-velocity transactions matching known smurfing typologies.
Core Engines
See What Others Miss
Traditional rule-based systems generate high false positives. GraphMule uses graph topology to identify the structure of financial crime.
Cycle Detection
Identify circular flows where money loops back to the originator, a classic sign of wash trading and synthetic fraud.
Smurfing Analysis
Detect fan-out (layering) and fan-in (integration) patterns used to break large sums into undetectable micro-transactions.
Shell Accounts
Pinpoint dormant or pass-through nodes that exist solely to move funds, characterizing them by degree and volume ratio.
Solutions
Enterprise-Grade Defense
AML Compliance
Automated monitoring for regulatory reporting and audit trails.
Payment Fraud
Real-time blocking of suspicious transactions before settlement.
Identity Theft
Detect synthetic identities through network link analysis.
Merchant Risk
Assess risk profiles of new and existing merchant accounts.
Crypto Tracing
Track illicit funds across blockchain hops and mixers.
Internal Audit
Identify employee collusion and embezzlement schemes.
Technology
Built for Hyperscale Intelligence
A modern stack designed for low-latency decisioning on billion-node graphs.
Graph Neural Engine
Proprietary Rust-based runtime executing complex traversals in microseconds.
fn detect_cycle(graph: &Graph) -> Result {
// efficient DFS with path pruning
let nodes = graph.scan_edges(Target::HighRisk);
await engine.process(nodes);
}
Real-time Ingestion
Connect via gRPC, Kafka, or REST. 100k+ events/sec throughput.
Enterprise Security
SOC2 Type II ready. End-to-end encryption and granular RBAC.
Seamless Integration
Works with your existing stack.