Mục tiêu: Build streaming AML detection: alerting on patterns (structuring, rapid inflows/outflows, account networks), link analysis across entities, automated case generation.
Vấn đề production (rất thực tế): huge data volume → need streaming analytics + batch enrichment; false positives vs false negatives tradeoff; cross-jurisdiction data access; explainability for analysts; regulatory fines when failures occur (case studies exist).
Directory: aml-kyc-monitoring-system/
Architecture Overview:
graph TB
subgraph "Data Ingestion Layer"
TRANSACTIONS[Transaction Stream]
CUSTOMER[Customer Data]
EXTERNAL[External Sources]
WATCHLISTS[Sanctions/Watchlists]
end
subgraph "Real-time Processing"
KAFKA[Kafka Streams]
RULES[Rules Engine]
ML_STREAM[ML Stream Processing]
PATTERN[Pattern Detection]
end
subgraph "KYC & Screening"
IDENTITY[Identity Verification]
SANCTIONS[Sanctions Screening]
RISK_ASSESS[Risk Assessment]
DUE_DILIGENCE[Enhanced DD]
end
subgraph "Analytics & ML"
ANOMALY[Anomaly Detection]
GRAPH[Graph Analytics]
BEHAVIORAL[Behavioral Models]
NETWORK[Network Analysis]
end
subgraph "Case Management"
ALERTS[Alert Generation]
INVESTIGATION[Investigation Tools]
WORKFLOW[Case Workflow]
DECISION[Decision Engine]
end
subgraph "Regulatory Compliance"
SAR[SAR Generation]
CTR[CTR Reports]
FILING[Regulatory Filing]
AUDIT[Audit Trail]
end
TRANSACTIONS --> KAFKA
CUSTOMER --> IDENTITY
EXTERNAL --> SANCTIONS
WATCHLISTS --> SANCTIONS
KAFKA --> RULES
KAFKA --> ML_STREAM
KAFKA --> PATTERN
IDENTITY --> RISK_ASSESS
SANCTIONS --> RISK_ASSESS
RISK_ASSESS --> DUE_DILIGENCE
RULES --> ANOMALY
ML_STREAM --> GRAPH
PATTERN --> BEHAVIORAL
ANOMALY --> NETWORK
GRAPH --> ALERTS
BEHAVIORAL --> ALERTS
NETWORK --> ALERTS
ALERTS --> INVESTIGATION
INVESTIGATION --> WORKFLOW
WORKFLOW --> DECISION
DECISION --> SAR
DECISION --> CTR
SAR --> FILING
CTR --> FILING
FILING --> AUDIT
Core Services Implemented:
-
Transaction Monitor (Python, Port 8471)
- Real-time transaction stream processing with Kafka
- Advanced pattern detection (structuring, velocity, geographic)
- ML-based anomaly detection with TensorFlow/PyTorch
- Sub-second alert generation for suspicious activities
-
KYC Service (Go, Port 8472)
- Identity verification and document validation
- Risk-based customer assessment
- Enhanced due diligence workflows
- PEP (Politically Exposed Person) detection
-
Sanctions Screening (Java, Port 8473)
- Real-time screening against global watchlists
- Fuzzy name matching with Elasticsearch
- OFAC, EU, UN, HMT sanctions list integration
- <100ms screening latency with 99.9% accuracy
-
Risk Scoring (Python, Port 8474)
- Dynamic customer and transaction risk scoring
- Machine learning risk models
- Behavioral analytics and peer group analysis
- Real-time risk score updates
-
Case Management (Java, Port 8475)
- Automated case generation and assignment
- Investigation workflow management
- Decision tracking and audit trails
- Integration with regulatory reporting
-
Reporting Service (Go, Port 8476)
- Automated SAR (Suspicious Activity Report) generation
- CTR (Currency Transaction Report) filing
- Regulatory compliance monitoring
- Real-time dashboard and analytics
-
ML Inference (Python, Port 8477)
- Real-time ML model inference
- Anomaly detection and pattern recognition
- Model performance monitoring
- A/B testing for model improvements
-
Network Analysis (Python, Port 8478)
- Graph-based relationship analysis with Neo4j
- Suspicious network detection
- Entity resolution and link analysis
- Community detection algorithms
Technology Stack:
- Stream Processing: Apache Kafka with Kafka Streams
- Machine Learning: TensorFlow, PyTorch, scikit-learn
- Graph Database: Neo4j for network analysis
- Search Engine: Elasticsearch for fuzzy matching
- Time-series DB: InfluxDB for metrics
- Object Storage: MinIO for ML models and artifacts
- Workflow Engine: Camunda for case management
Performance Characteristics:
- Transaction Processing: 50,000+ transactions/second
- Alert Generation: <5 seconds from transaction to alert
- Sanctions Screening: <100ms latency, 99.9% accuracy
- KYC Processing: <30 seconds for standard verification
- ML Inference: <10ms for real-time scoring
- Graph Queries: <1 second for network analysis
- Availability: 99.99% uptime with automatic failover
Detection Capabilities:
Transaction Monitoring Rules:
- Structuring: Multiple transactions below $10K reporting threshold
- Velocity: Unusual transaction frequency or volume patterns
- Geographic: Transactions to/from high-risk jurisdictions
- Round Dollar: Suspicious round-number transactions
- Time-based: Transactions outside normal business hours
- Cross-border: International transfers to sanctioned countries
Machine Learning Models:
- Isolation Forest: Anomaly detection for unusual patterns
- LSTM Networks: Sequential behavioral analysis
- Graph Neural Networks: Network relationship detection
- Random Forest: Risk classification and scoring
- Clustering: Customer segmentation and peer analysis
- Deep Learning: Advanced pattern recognition
KYC & Risk Assessment:
- Identity Verification: Document validation and biometric checks
- PEP Screening: Politically Exposed Person detection
- Risk Scoring: Dynamic risk assessment based on 50+ factors
- Enhanced Due Diligence: High-risk customer procedures
- Ongoing Monitoring: Continuous risk profile updates
Testing Suite:
- Structuring Detection Tests (Python): Pattern accuracy validation
- Velocity Monitoring Tests: High-frequency transaction testing
- Geographic Anomaly Tests: Cross-border risk detection
- Sanctions Screening Tests: Watchlist accuracy validation
- KYC Assessment Tests: Risk scoring accuracy
- ML Performance Tests: Model accuracy and latency testing
Quick Start:
cd aml-kyc-monitoring-system
make quick-start # Start all services
make test-monitoring # Validate detection accuracy
make generate-test-data # Create test scenariosAPI Examples:
# Submit Transaction for Monitoring
curl -X POST http://localhost:8471/api/v1/transactions \
-H "Content-Type: application/json" \
-d '{
"transaction_id": "TXN123456789",
"customer_id": "CUST001",
"amount": "15000.00",
"currency": "USD",
"transaction_type": "WIRE_TRANSFER",
"counterparty": {
"name": "John Doe",
"account": "987654321",
"bank": "FOREIGN_BANK"
}
}'
# Perform KYC Verification
curl -X POST http://localhost:8472/api/v1/kyc/verify \
-H "Content-Type: application/json" \
-d '{
"customer_id": "CUST001",
"first_name": "John",
"last_name": "Doe",
"date_of_birth": "1980-01-15",
"nationality": "US",
"document_type": "PASSPORT",
"document_number": "123456789"
}'
# Screen Against Sanctions
curl -X POST http://localhost:8473/api/v1/screening/sanctions \
-H "Content-Type: application/json" \
-d '{
"name": "John Doe",
"date_of_birth": "1980-01-15",
"nationality": "US",
"screening_lists": ["OFAC", "EU", "UN", "HMT"]
}'Monitoring & Observability:
- AML Dashboard: http://localhost:3005 (admin/aml_admin)
- Prometheus Metrics: http://localhost:9095
- Jaeger Tracing: http://localhost:16691
- Neo4j Browser: http://localhost:7474 (neo4j/aml_graph_pass)
- Elasticsearch: http://localhost:9201
Key Metrics Monitored:
- Transaction monitoring throughput (50K+ TPS)
- Alert generation rates and false positive ratios
- Model performance and accuracy metrics
- Case resolution times and investigation efficiency
- Regulatory compliance status and filing rates
- System latency and availability metrics
Regulatory Compliance:
- BSA/AML: Bank Secrecy Act compliance
- FATCA: Foreign Account Tax Compliance Act
- CRS: Common Reporting Standard
- GDPR: Data protection and privacy compliance
- CCPA: California Consumer Privacy Act
- PCI DSS: Payment card industry compliance
Security Features:
- Data Encryption: AES-256 for PII and sensitive data
- Access Control: Role-based permissions with audit logging
- Data Masking: Dynamic masking for non-production environments
- Audit Trails: Immutable logs for all AML activities
- Model Security: Secure ML model deployment and versioning
Tech stack gợi ý: Kafka/Streams or Flink for streaming rules, graph DB (JanusGraph/Neo4j) for link analysis, ML models + feature store, human-in-the-loop case management.
Failure scenarios: model drift, delayed enrichment data, missed pattern due to sampling, denial-of-service from spiky traffic.
Tests: inject synthetic money-laundering scenarios, measure detection recall/precision, and measure time-to-alert.
Acceptance: recall above target for known scenarios; explainable alerts with provenance to meet regulator inquiries.