30–50%
False-positive alert reduction
Full Coverage
MAR/MiFID II typologies
$2.9B
Industry surveillance penalties (24 months)
Exam-ready
Methodology documentation
Client Snapshot
Industry
Capital Markets
Solution
AI, GenAI & ML | Data Solutions
Complexity
High
Delivery
Architecture + Implementation
The Problem
Surveillance programs at most mid-tier broker-dealers are rule-heavy and alert-light hundreds of active detection rules generating alert volumes that analyst teams cannot fully review within policy-defined timeframes. When surveillance alert backlogs exceed review capacity, the program is operationally non-compliant even if documentation suggests otherwise. SEC and FINRA examiners have significantly increased trade surveillance scrutiny since 2022, focusing on whether programs generate appropriate alerts and whether those alerts are reviewed on time.
Market abuse patterns have evolved beyond static rule detection capability. Cross-product manipulation linking equity and options positions, layering schemes that exploit fragmented execution across multiple venues, and spoofing patterns that adapt to known detection rules all require detection approaches that combine multiple data streams across extended time windows. AI-driven surveillance that learns evolving manipulation patterns consistently outperforms static rule coverage for these complex schemes.
Ready to Start?
Schedule a Surveillance Program Assessment
Get a candid analysis of your current detection coverage, alert backlog, and examination readiness.
$2.9B
in SEC and FINRA enforcement penalties related to surveillance failures in a 24-month period. The most common finding: surveillance programs with documented rule coverage that failed to demonstrate alerts were reviewed within policy-defined timeframes. Alert backlog is the primary enforcement driver not coverage gaps.
How PiTech Delivers
01
Surveillance Coverage Assessment
Current rule library mapped to MAR Article 12, MiFID II, and FINRA manipulation typologies. Coverage gaps identified. False-positive rate analyzed by rule family and asset class. Output: prioritized enhancement roadmap with estimated alert volume impact.
02
AI-Augmented Detection Deployment
Machine learning models trained on historical surveillance outcomes for complex patterns layering, spoofing, cross-product manipulation, wash trading that static rules consistently miss. Unsupervised anomaly detection layer for emerging manipulation behaviors outside training history.
03
Alert Management Workflow Optimization
Pre-enrichment pipeline supplies investigation templates with relevant order, execution, communication, and position data before the alert reaches an analyst. Investigation time per alert reduced, enabling higher throughput without additional headcount addressing backlog compliance directly.
04
Regulatory Documentation Package
Surveillance methodology documentation, detection coverage matrix, alert review workflow evidence, and investigation completion records structured for SEC, FINRA, FCA, and ESMA examination review. Documentation produced as a standard program deliverable, not assembled reactively before examination.
Proven Outcomes
30–50%
False-positive alert reduction in deployed surveillance programs
Full
MAR/MiFID II manipulation typology coverage in every engagement
18+ yrs
Capital markets regulatory experience SEC, FINRA, FCA, ESMA expertise
Proven Outcomes
18+
Years in Regulated Industries
What You Gain
30–50%
False-positive alert reduction through AI-augmented detection
Full
MAR/MiFID II manipulation typology coverage including cross-product schemes
Documented
Surveillance methodology and coverage matrix for regulatory examination
Reduced
Average analyst investigation time per alert through pre-enrichment
What's Included
Surveillance coverage matrix
Surveillance coverage matrix
AI detection models
AI detection models
Cross-asset surveillance data model
Cross-asset surveillance data model
Alert pre-enrichment pipeline
Alert pre-enrichment pipeline
Alert review workflow management
Alert review workflow management
Communication surveillance integration
Communication surveillance integration
Regulatory methodology documentation
Regulatory methodology documentation
Frequently Asked Questions
What market abuse typologies does PiTech's AI surveillance detect?
PiTech deploys detection models for layering and spoofing (single and cross-venue), front-running, cross-product manipulation linking equity and derivatives positions, wash trading, marking the close, and painting the tape. An unsupervised anomaly detection layer handles novel manipulation patterns outside the historical training distribution.
How does communication surveillance integrate with trade surveillance?
PiTech integrates voice transcript, electronic messaging, and chat data with trading activity records for contextual alert enrichment. Examiners expect surveillance programs to correlate trader communications with potentially abusive trading patterns PiTech’s architecture delivers this correlation as a standard investigation context component rather than a separate manual process.
What do SEC and FINRA examiners specifically look for in trade surveillance programs?
Examiners focus on three dimensions: coverage (are the right manipulation typologies being detected), review (are all alerts being reviewed within policy-defined timeframes with complete documentation), and methodology (can the firm demonstrate the detection rationale is sound and the program is risk-based). PiTech designs programs to satisfy all three dimensions explicitly.
Can PiTech improve an existing surveillance program without replacing the current platform?
Yes. PiTech can enhance existing rule libraries, add AI detection layers on top of current surveillance platforms (NICE Actimize, SMARTS, Nasdaq Surveillance), and improve alert management workflows without platform replacement the most cost-effective path for firms with established surveillance infrastructure.
How does PiTech address the alert backlog compliance problem specifically?
Alert backlog is a workflow design problem, not a staffing problem at most firms. PiTech addresses it through three levers: reducing alert volume through AI-based false-positive reduction, reducing investigation time per alert through pre-enrichment automation, and implementing workflow prioritization that ensures policy-defined timeframe compliance for high-priority alerts first.
Trade surveillance program quality is a direct measure of regulatory relationship health. PiTech builds programs that satisfy examiners and protect the firm.
Contact PiTech to begin with a surveillance coverage and backlog assessment specific to your product mix and regulatory jurisdiction.
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Address
4000 Sancar Way, Suite 205, Durham, NC 27709
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Contact Details
(919) 439-3163
