From compliance to differentiation: Rethinking surveillance in financial services

• Financial institutions are shifting surveillance from a reactive, compliance-driven function to a proactive, integrated capability that uses analytics, behavioral insights, and cross-channel data to anticipate risks before they escalate.
• Advancements in AI, machine learning, and unified surveillance platforms are enabling firms to reduce false positives, improve investigation quality, and deliver meaningful conduct insights to leadership, regulators, and the business.
• When embedded strategically, surveillance becomes a competitive differentiator, strengthening trust with regulators and clients, informing enterprise risk decisions, and driving operational efficiency across the organization.

For years, surveillance in financial services has been treated as a defensive necessity—an operational function focused on monitoring trading, communications, and conduct to meet regulatory obligations. But as markets evolve, regulators intensify scrutiny, and technology reshapes operations, forward-looking firms are reframing surveillance as more than compliance. When executed strategically, surveillance becomes a competitive differentiator, driving efficiency, strengthening trust, and providing actionable business intelligence.

Traditionally, surveillance has been reactive exercise in risk management. Teams respond to alerts, often in siloes, dealing with high false positives, fragmented systems, and backlogs. Most surveillance tools, like e-communications, voice, and trade, operate independently and limit visibility. As a result, surveillance is viewed largely as a cost center, focused on remediation and compliance rather than delivering insight or strategic value to the business.

Proactive surveillance shifts from alert-chasing to risk sensing by using integrated data, context, and analytics to identify issues before they escalate. Key enablers of proactive surveillance include:

• Horizontal visibility: Link trade events to communications to detect intent; for example, a pattern of trading behavior contextualized by off-platform messages or coded language.
• Behavioral and network analytics: Move beyond simple rule breaches to detect anomalous behavior, social graph patterns, and collusive activity across desks, entities, and instruments.
• Scenario-based testing: Use risk typologies and emerging market behaviors to simulate threats, refine coverage, and prioritize controls.
• Early-warning indicators: Develop composite risk scores combining alert density, case conversion, exception trends, and conduct signals to trigger proactive reviews or targeted training.
• Closed-loop learning: Feed outcomes such as true/false positives, enforcement results, and supervisory feedback back into models, tuning thresholds, and typologies over time.

Proactive programs materially reduce remediation costs and cycle times, elevate the quality of investigations, improve signal-to-noise, and give senior leaders a predictive view of conduct risk. They also strengthen the firm’s culture by aligning front office, compliance, risk, and audit around shared insights and metrics.

Financial services firms should move more towards integrated surveillance by linking monitoring of trades, electronic communications, and voice communications to break silos and create a holistic picture of conduct risk. This can be accomplished by building an interface or module that creates linkages to monitor risks across platforms.

AI and machine learning bring robust new monitoring capabilities. For example, in the case of spotting a suspicious trade that is linked to an email or chat, AI can reduce false positives and detect unusual behavior patterns, not just rule breaches. Predictive monitoring can identify emerging risks before they become systemic. The development of dashboards capturing all the distinct types of surveillance conducted at a firm can provide senior management with insights into conduct exposure, help tailor training, and improve overall enterprise risk management.

Robust surveillance programs demonstrate to regulators that firms are proactive and ahead of the curve. In fact, regulators are pushing for a shift from box-checking and alert-chasing toward more integrated, intelligence-led surveillance that delivers real insight and accountability. In addition, clients and investors increasingly view strong conduct and culture frameworks as integral to fiduciary responsibility. Surveillance becomes a pillar of trust, protecting clients, safeguarding markets, and reinforcing the firm’s reputation.

Broker-dealers, investment advisers, and asset managers are moving beyond “check the box” surveillance. They are embedding surveillance as a strategic advantage by:

• Investing in scalable, cloud-native platforms: Standardized data models, reusable integrations, and elastic compute to support global coverage and faster onboarding of new channels/products.
• Applying behavioral analytics to anticipate misconduct: Detecting intent and patterns—front-running, spoofing, layering, wash trading, off-channel communications—before they trigger incidents.
• Integration with enterprise risk management (ERM): Feeding conduct insights into risk appetite, stress testing, capital planning, and issues management for a consistent risk narrative.
• Operationalizing insight for the business: Providing front-office supervisors with targeted signals, peer benchmarks, and coaching prompts that improve conduct and reduce supervisory burden.
• Improved visibility to leadership: Strategic dashboards for senior management and the Board that highlight emerging themes and control effectiveness.

Making surveillance strategic

Surveillance in financial services is no longer just a compliance requirement. Leading institutions focus on three areas to make it a source of strategic value:

1. Program design

Assess current surveillance programs to identify gaps and redesign operating models that align with both risk and business goals.

2. Technology integration

Unify trade, e-comms, and voice data. Use AI and machine learning to improve detection and triage, and surface insights through real-time dashboards.

3. Operational efficiency

Continuously tune thresholds, reduce false positives, and apply flexible models—from surge support to managed services—for scalable review and improvement.

Surveillance is no just about monitoring the past. If done correctly, it anticipates patterns and potential issues, leading to better detection and prevention. Firms that elevate surveillance into a strategic capability will not only stay compliant but also create long-term value, differentiating themselves in an increasingly competitive and transparent marketplace.