Post-Trade Compliance

Core Concepts

Trade Surveillance Framework

Trade surveillance is the systematic, ongoing monitoring of executed transactions to detect potential violations of securities laws, firm policies, and regulatory rules. A surveillance program operates across multiple time horizons:

• T+0 (same-day) monitoring — Real-time or end-of-day reviews targeting time-sensitive patterns such as front-running (trading ahead of a customer block order), late trading (mutual fund orders placed after the 4:00 p.m. ET NAV pricing cutoff), and marking the close (orders placed to influence the closing price). T+0 alerts require immediate investigation because the regulatory harm is ongoing or the evidence window is narrow.
• T+1 through T+3 monitoring — Next-day and settlement-window reviews for patterns that emerge across a short sequence of events: allocation fairness on block trades, partial fill distribution, and settlement failures. These alerts align with the trade settlement cycle and CAT error correction windows.
• T+N rolling-window monitoring — Longer-horizon reviews (weekly, monthly, quarterly) for patterns that only become visible over time: churning and excessive trading (turnover ratios measured over months), coordinated trading across accounts, systematic favoritism in allocations, and insider trading correlations (trading patterns around earnings announcements or M&A events). Rolling windows must be calibrated to the specific pattern — churning detection typically requires 3-12 months of data, while insider trading correlation windows may span 30-90 days around a material event.

Surveillance scope varies by firm type and business activity. A full-service broker-dealer conducting equities, fixed income, and derivatives trading must maintain surveillance across all asset classes. An RIA managing model portfolios may focus surveillance on allocation fairness, best execution, and personal trading. The surveillance program must cover both customer/client accounts and proprietary/firm accounts.

Alert generation is the process of applying quantitative thresholds, pattern matching rules, or scoring models to transaction data to produce alerts requiring human review. Effective alert generation requires clean, normalized data from multiple sources: order management systems, execution management systems, account master data, market data, and — for insider trading detection — corporate event calendars and restricted lists.

Investigation workflow follows a standard lifecycle: