Real-Time AML Transaction Monitoring Engine
Architected a streaming AML transaction engine using Apache Flink and Neo4j graph networks, processing 4M+ daily transfers and dropping false positives from 85% to 22%.
4M+ Daily Wires22% False Positive Rate3x Analyst Capacity
Key Takeaways
- Graph entity resolution in Neo4j exposed multi-hop layering schemes invisible to isolated transaction threshold rules.
- Real-time Apache Flink stream evaluation reduced AML alert backlogs from 30+ days to under 2 days.
- Automated Suspicious Activity Report (SAR) pre-population tripled compliance analyst investigation capacity.
The Challenge
A global payments network processing 4M+ wire transfers daily generated 12,000 daily anti-money laundering alerts under rigid legacy threshold rules. 85% of alerts were false positives, causing a 30-day case backlog and regulatory compliance audit vulnerability.
Architecture & Technical Approach
- Streaming Feature Engine: Apache Flink evaluates 200+ velocity and risk features in under 50ms per wire transfer.
- Graph Entity Resolution: Neo4j clusters account beneficiaries, shared device fingerprints, and corporate shell registries across 3 hops.
- Automated Dossier Generation: Auto-compiles transaction ledgers, network graphs, and narrative summaries into draft SAR filings for compliance officers.
Quantitative Benchmarks & Results
| AML Operations Metric | Legacy Rules Engine | Streaming Graph Pipeline | Operational Lift |
|---|---|---|---|
| Daily Alert Generation Volume | 12,000+ Alerts | 3,200 Alerts | 73.3% Noise Reduction |
| False Positive Alert Rate | 85.0% | 22.0% | 74.1% Precision Improvement |
| Compliance Investigation Backlog | 30+ Days | < 2 Days | 93.3% Backlog Compression |
| Analyst Case Resolution Rate | 8 Cases/Day | 24 Cases/Day | 3.0x Investigation Capacity |
Production Reliability & Lessons Learned
Dynamic customer risk profiling that accounts for historical business volumes eliminated the majority of false positive triggers on legitimate enterprise payroll batches.