Trade exception events occur at high volume, and long-tail edge cases dominate. As a result, manual investigation is slow and tribal-knowledge driven. This is a very poor candidate for automation with legacy solutions like RPA, but makes for a good candidate for AI agentic automation
Workflow goal
Resolve trade and settlement exceptions efficiently by reasoning across systems and documenting outcomes.
Outputs & Metrics
Exception aging
STP improvement
Repeat exception reduction
Ops cost per trade
End-to-End Agentic Flow
1
Exception Detection
• Break in trade, settlement, or corporate action
2
Root Cause Investigation
• Compare across OMS, EMS, custodian, counterparty
3
Hypothesis Generation
• Timing mismatch, data error, corporate action nuance
4
Resolution Recommendation
• Correct data, rebook trade, contact counterparty
5
Execution
• Prepare updates or communications
6
Documentation
• Log reasoning and outcome
Platform features highlighted
Non-deterministic reasoning
Hypothesis testing
Cross-system orchestration
Safe automation under uncertainty
Control Points
Write access tightly scoped
Human approval before financial impact
Full traceability