Market abuse detection requires speed and precision. This blueprint presents a next-generation sovereign multi-agent trade surveillance system that dramatically improves detection while reducing false positives.
Business Challenge Many Organisations want to build internal AI capabilities but lack a proven, governed approach to creating and scaling a sovereign digital workforce.
UNLOCK FULL USE CASE + PDFExecutive Summary / Key Takeaways
- 5–10x improvement in true positive detection
- Up to 70% reduction in false positives
- Behavioural pattern recognition across multiple data sources
- Automated investigation workflows with LangGraph
- Complete sovereignty and auditability for Swiss regulated entities
The Challenge
High false positive rates, slow detection of complex schemes, and overwhelming compliance teams
Our Approach / Framework
Multi-agent system with Transaction Behavior, Network Analysis, Anomaly Detection, Automated Investigation, and Regulatory Reporting agents.
Technical Architecture
LangGraph stateful orchestration, Ollama inference, Qdrant pattern memory, n8n integrations in Swiss namespaces.
Implementation Guide
10-week roadmap
Conclusion & Future Outlook
Transform trade surveillance from a reactive cost centre into a proactive intelligent defence.
Key Takeaways
- 5–10x improvement in true positive detection
- Up to 70% reduction in false positives
- Behavioural pattern recognition across multiple data sources
- Automated investigation workflows with LangGraph
- Complete sovereignty and auditability for Swiss regulated entities
