Monitoring sensors.
Ongoing transaction and activity monitoring driven by configurable rules and scenarios, testable against your own historical data
Overview
The Monitoring system provides continuous transaction and suspicious-activity monitoring through a configurable rule and scenario engine, surfacing risk early.
Detection scenarios you configure run on a scheduled cadence — every five minutes — over ingested and imported transactions, so suspicious patterns surface shortly after the activity reaches the platform.
Every rule can be tested against your historical data before it goes live, and every alert traces back to the exact logic that raised it — transparent, examiner-friendly detection.
Key Features
Configurable, testable detection across your transaction activity.
Rule & Scenario Engine
Deterministic rules and scenarios you configure in the UI to detect suspicious patterns — transparent, examiner-friendly logic.
Scheduled Monitoring
Detection scenarios run on a five-minute scheduled cadence over ingested and imported transactions.
Historical Backtesting
Test rules against your historical data and review rule-performance statistics before enabling them.
Benefits
Integrations
How Monitoring connects with the systems around it — inbound activity feeds, downstream destinations, APIs, and the jobs that keep detection on schedule.
- Core banking extracts
- Transaction bulk-ingest API
- Imported transaction files
- Client profile data
- Alert management
- Case management
- Regulatory reporting
- Transaction ingest API
- Alert management API
- Case management API
- Scenario execution · 5-minute cadence
- Rule execution
- Alert escalation & SLA checks
Performance Metrics
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Frequently Asked Questions
What does Monitoring track?
Monitoring evaluates the transactions your institution loads — wires, payments, cash — against the rules and scenarios you configure, raising alerts on hits.
How does detection work?
Detection is a deterministic rule and scenario engine: you define conditions in the UI, test them against historical data, and every alert traces back to the exact logic that fired. AI in AmlShield is a separate, opt-in assist layer for investigation summaries and narratives — audited on every use, never making detection decisions.
Is monitoring real-time?
Monitoring runs as scheduled jobs on a five-minute cadence rather than inside the payment path. In practice, suspicious activity surfaces within minutes of reaching the platform.
What transaction volume can it handle?
Detection runs over the transactions your institution brings in through the bulk-ingest API and file imports; capacity is assessed for your data volumes during implementation.
How does Monitoring connect to alerts and cases?
Scenario hits are routed into alert management for triage with severity and SLA, and confirmed suspicious activity flows into case management with complete audit trails.
Can detection thresholds be tuned?
Yes. Rule conditions, thresholds, and scenario parameters are fully configurable, and you can backtest any change against historical data before enabling it.
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