API Design for Real-Time AML Integration

API Design Requirements

Real-time AML APIs must balance competing demands: low latency, high throughput, reliability, and ease of integration. Get the API design wrong, and you'll either bottleneck transaction processing or compromise detection accuracy.

Core API Endpoints

1. Transaction Screening

Request:
{
  "transaction_id": "txn_abc123",
  "amount": 9850.00,
  "currency": "USD",
  "timestamp": "2025-09-03T14:23:45Z",
  "sender": {
    "entity_id": "ent_sender_456",
    "account_id": "acc_789"
  },
  "receiver": {
    "entity_id": "ent_receiver_321",
    "account_id": "acc_654"
  },
  "metadata": {
    "channel": "mobile",
    "ip_address": "192.0.2.1",
    "device_id": "dev_xyz"
  }
}

Response (< 100ms):
{
  "risk_score": 87,
  "risk_level": "HIGH",
  "decision": "REVIEW",
  "explanation": {
    "primary_factors": [
      "Amount just below $10K threshold (+32 points)",
      "Transaction velocity 12x higher than average (+25 points)",
      "New counterparty (+18 points)"
    ]
  },
  "case_id": "case_2025_09_00145"
}

2. Batch Screening

Request:
{
  "transactions": [ /* array of 1-10,000 transactions */ ],
  "callback_url": "https://your-system.com/aml-results"
}

Immediate Response:
{
  "batch_id": "batch_2025_09_001",
  "status": "PROCESSING",
  "estimated_completion": "2025-09-03T14:28:00Z"
}

Callback (when complete):
{
  "batch_id": "batch_2025_09_001",
  "results": [ /* risk scores for each transaction */ ]
}

3. Entity Risk Lookup

Response:
{
  "entity_id": "ent_456",
  "risk_score": 42,
  "risk_level": "MEDIUM",
  "factors": {
    "historical_sars": 1,
    "avg_transaction_size": 2500,
    "velocity_90d": 47,
    "network_centrality": 0.23
  },
  "last_updated": "2025-09-03T12:00:00Z"
}

Performance Optimization

Latency Targets

• p50 latency: < 50ms
• p95 latency: < 100ms
• p99 latency: < 200ms
• Timeout: 500ms (fail open with alert)

Caching Strategy

• Entity Profiles: Redis cache, 5-minute TTL
• Network Features: Pre-computed, updated hourly
• Sanctions Lists: Cached locally, refreshed daily
• Model Weights: Loaded in memory, hot-swapped on update

Circuit Breaker Pattern

Protect downstream services from cascading failures:

Circuit States:
1. CLOSED (normal): All requests processed
2. OPEN (failure): Requests fail fast, no processing
3. HALF_OPEN (recovery): Test requests to check if service recovered

Thresholds:
- Open circuit after 10 consecutive failures OR 50% error rate in 30s
- Half-open after 60s
- Close after 5 successful requests in half-open state

Reliability & Resilience

Graceful Degradation

If full ML pipeline fails, fall back to simpler models:

1. Primary: Full ensemble (GNN + LSTM + Isolation Forest)
2. Fallback 1: Simplified model (logistic regression) if GPU unavailable
3. Fallback 2: Rule-based scoring if ML infrastructure down
4. Fallback 3: Fail open with alert to compliance team

Idempotency

Clients can safely retry requests with same transaction_id:

• API deduplicates based on transaction_id + timestamp
• Return cached result if request already processed
• Idempotency window: 24 hours

Rate Limiting & Throttling

Security

Authentication

• API Keys: For server-to-server communication
• OAuth 2.0: For user-facing applications
• mTLS: Mutual TLS for high-security deployments

Data Protection

• TLS 1.3: All data in transit encrypted
• Field-Level Encryption: Sensitive PII encrypted in requests/responses
• Audit Logging: All API calls logged with retention policy
• Data Residency: Regional endpoints for GDPR compliance

Monitoring & Observability

Comprehensive metrics exposed for monitoring:

Distributed Tracing

End-to-end request tracking with OpenTelemetry:

• API Gateway → Feature Store → ML Inference → Response
• Identify bottlenecks and slow dependencies
• Track requests across microservices

API Versioning

Multiple API versions supported concurrently:

• URL-based: /v1/, /v2/ in path
• Deprecation Policy: 12-month notice before sunset
• Breaking Changes: Only in major versions
• Backward Compatibility: Maintained within major version

SDK & Client Libraries

Official SDKs for common languages:

from nerous import AMLClient

client = AMLClient(api_key="your_api_key")

# Screen single transaction
result = client.screen_transaction({
    "transaction_id": "txn_123",
    "amount": 9850,
    "currency": "USD",
    "sender": {"entity_id": "ent_456"},
    "receiver": {"entity_id": "ent_789"}
})

print(f"Risk Score: {result.risk_score}")
print(f"Decision: {result.decision}")

# Batch screening
batch = client.screen_batch(transactions)
batch.wait_for_completion()
results = batch.get_results()

Testing & Development

Sandbox Environment

• Test API Keys: No charges, limited rate limits
• Synthetic Data: Pre-loaded test scenarios
• Mock Responses: Simulate different risk levels
• Latency Simulation: Test timeout handling

Webhooks

Subscribe to events for asynchronous updates:

• case.created: New high-risk case generated
• case.updated: Analyst action on case
• model.updated: New model version deployed
• alert.triggered: Threshold breach notification

Integration Best Practices

1. Async Where Possible: Use batch API for non-blocking workflows
2. Implement Retries: Exponential backoff for transient failures
3. Handle Timeouts: Don't block user experience on slow responses
4. Cache Aggressively: Entity risk lookups can be cached locally
5. Monitor Closely: Track latency, errors, and fallback activations

Conclusion

API design for real-time AML is as much about operational excellence as technical capability. At nerous.ai—where our Finnish name reflects ingenuity and brilliance—we've built APIs that deliver sub-100ms latency at 100M+ requests/day while maintaining 99.99% uptime.

The result: seamless integration into transaction flows that enhances security without compromising user experience or system performance.