LSEG builds AI-powered Surveillance Guide on Amazon Bedrock to classify price-sensitive news and detect market abuse
LSEG's market surveillance analysts faced a time-consuming manual triage process: after automated alerts flagged suspicious trading, analysts had to manually collate evidence and assess news price sensitivity. High false-positive alert volumes degraded analyst quality time and risked operational delays.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Surveillance alert triggers review
LSEG's surveillance monitoring systems generate automated, customized alerts to flag suspicious trading activity to the Market Supervision team.
Tools used
Claude Sonnet 3.5Amazon SageMakerJupyter NotebooksStreamlitInstructor libraryPythonRegulatory News Service (RNS)
Outcome
Over a 6-week evaluation period, the Surveillance Guide achieved 100% precision for identifying non-sensitive news and 100% recall for detecting price-sensitive content, reducing manual review time and enabling analysts to focus on the most critical cases.
What failed first
Existing automated rules-based surveillance systems were outdated and generated an increasing number of false-positive alerts, overwhelming analysts with low-value cases and becoming inadequate as market tactics evolved.