Reducing noise and improving risk detection in AI market surveillance
Guidehouse helped a global investment bank implement AI-driven market surveillance, improving alert quality, consistency, and usability.
Summary
A global investment bank seeking to improve its market surveillance capabilities by implementing an AI-enabled alerting model turned to Guidehouse for assistance. Our support with model input and output validation, detection logic refinement, and investigator workflow optimization helped improve alert quality, reduce noise, and increase reviewer efficiency.
Surveillance program improvements
50%
of the team trained across multiple workflows
50%
increase in analyst throughput
40%
reduction in false-positive escalation rate
Challenge
As leaders introduced AI into the bank’s market surveillance program, they needed confidence that model outputs were accurate, actionable, and aligned with existing compliance workflows.
Variability across surveillance data sources was affecting alert quality, while immature routing logic and escalation thresholds were reducing effectiveness during testing. At the same time, AI-generated summaries often lacked the clarity and consistency needed for investigators to efficiently review and disposition alerts. Without structured validation and iterative refinement, these challenges created an increased risk of higher false positives and lower reviewer efficiency across insider trading and market manipulation detection workflows.
Approach
In partnership with the client’s teams, our experts validated, optimized, and operationalized an AI-enabled market surveillance model within a complex compliance environment. Our approach focused on strengthening data quality, improving model performance, and enhancing the investigator experience.
To improve consistency across alert generation, routing, and investigative processes, we validated surveillance data inputs and workflow integrations. We also evaluated risk indicators, escalation thresholds, and model outputs to enhance identification of potentially suspicious trading activity while reducing unnecessary noise. Through iterative testing and user feedback, we refined AI-generated summaries and narrative outputs, improving clarity, consistency, and usability for investigators responsible for alert review and disposition.
Impact
Guidehouse supported the deployment of an AI-enabled market surveillance model that reduced alert noise and enhanced the quality and consistency of alerting criteria. Improved risk detection and escalation workflows increased alert-to-case conversion rates, helping investigators focus on the most significant trading risks.
The initiative also streamlined surveillance operations through standardized workflows and more efficient review processes. This enabled broader cross-training across surveillance functions, increasing program agility and operational resilience.
The result was a more effective and scalable surveillance program that strengthened risk oversight, improved investigator effectiveness, and supported compliance in a complex regulatory environment.