Missed Detection and the FINRA 3110 Coverage Gap
Under FINRA Rule 3110 and SEC Rule 10b-5, broker-dealers are expected to maintain supervisory systems reasonably designed to detect and investigate potential violations. A surveillance workflow that leaves 80% of the daily queue unreviewed is a documented gap — and in the event of a regulatory inquiry or enforcement action, that documentation works against the firm. The practical challenge is that NICE Actimize alert volumes aren't going down. As trading activity grows and lexicon-based communication review from Smarsh and Global Relay adds its own alert stream, the queue grows faster than headcount. Surveillance analysts are making disposition decisions on the alerts they get to, but the backlog accumulates.
Auto-Scoring Alerts with Communication Context and Market Data
An AI Labor Company agent mines surveillance analysts' historical alert triage decisions from NICE Actimize — the scoring patterns, the communication flags from Smarsh that elevated cases, the Bloomberg market data correlations that distinguished legitimate hedging from manipulative sequences. The deployed agent auto-scores incoming alerts using this learned logic, pulling communication context from Smarsh and Global Relay alongside real-time Bloomberg data to assess each alert in the context analysts would apply manually. Low-risk alerts are auto-dispositioned with a fully documented rationale that satisfies FINRA supervisory recordkeeping requirements. High-risk cases are routed to surveillance analysts for same-day review. The result is 100% queue coverage — not a statistical improvement on 20%, but actual closure of the gap.
The Business Case: Regulatory Risk Avoidance and Team Leverage
The primary value here is risk avoidance with a measurable floor: the cost of a FINRA enforcement action or SEC inquiry for inadequate supervisory procedures is measured in settlement amounts, remediation plans, and regulatory scrutiny that persists for years. Shifting from 20% to 100% daily alert coverage is a defensible supervisory posture that didn't previously exist at this alert volume without proportional headcount. There is also a team leverage story. Surveillance analysts focused on the high-risk cases the agent surfaces are doing higher-value compliance work — the investigations that require judgment, context, and regulatory knowledge. The agent handles the volume; analysts handle the substance. Implementation typically takes about 10 weeks.
What documentation does the agent generate for auto-dispositioned alerts to support FINRA supervisory recordkeeping?
Each auto-disposition includes the alert type, the scoring rationale referencing the specific communication and market data inputs, the risk classification applied, and a timestamp. The documentation is formatted for Actimize audit trail integration and is designed to withstand a FINRA examination review.
How does the agent handle new lexicon patterns or market manipulation schemes it hasn't seen in training data?
Novel patterns that don't match the trained disposition logic are escalated to a surveillance analyst rather than auto-dispositioned. The agent is designed to err toward human review when confidence is low — not to disposition cases it can't score with sufficient certainty.
Can the system be tuned to reflect changes in the firm's internal surveillance policies over time?
Yes — the agent's scoring logic is updated through a supervised feedback loop where analysts review and validate disposition decisions. Policy changes are incorporated through a structured update process rather than requiring a full rebuild.