Trade Surveillance Is Facing Connected Challenges
Surveillance teams are assessing AI alongside broader operational and regulatory pressures. Data must provide a complete view of the order lifecycle. Models need to support investigations that can be explained with confidence. Tokenized assets can introduce new relationships between traditional and digital markets. Cross-border obligations can create different requirements for the same activity.
These challenges affect how firms select technology, organize teams, and define surveillance workflows.
Data Completeness and Order Lifecycle Context
Data quality remains central to surveillance. Teams need sufficient context to reconstruct activity from order creation through modification, cancellation, and execution. Without that context, firms can struggle to investigate behaviors such as layering or spoofing, and they may generate alerts that do not reflect the full sequence of events.
The webinar discusses why many teams are focusing on completeness, coverage, and accurate order lifecycle data when reviewing their surveillance approach.
AI Adoption, False Positives, and Explainability
The 2026 Trade Surveillance Benchmark shows that AI adoption is uneven. Some firms have AI in production, while others are evaluating use cases or have not yet adopted it. The webinar considers the gap between the promise of false-positive reduction and the measurable value teams are seeing in practice.
The session also examines explainability. A model may identify a pattern that needs investigation, yet surveillance and technology teams still need to explain why the model produced that result. This creates questions about governance, skills, validation, and communication between technical teams and surveillance leadership.
Tokenized Assets and Cross-Asset Monitoring
Tokenization adds complexity to trade surveillance when activity spans traditional and digital markets. A related instrument may exist across different venues or forms, raising questions about data access, detection logic, and accountability for identifying potential cross-asset abuse.
The webinar explores how teams are approaching tokenized-asset coverage, the current uncertainty around responsibilities, and the practical limits of existing surveillance technology.
Regulatory Divergence and Cross-Border Obligations
Regulatory expectations can differ across jurisdictions. Firms operating across markets may need to manage separate definitions, reporting requirements, data considerations, and surveillance priorities.
This discussion considers how regulatory divergence affects technology and operating-model decisions, including the need for clear requirements that surveillance teams can apply consistently.
What You’ll Learn
- Why data completeness and order lifecycle context matter for surveillance investigations
- How firms are approaching AI adoption and false-positive reduction
- Why explainability remains a key consideration for AI-assisted surveillance
- What tokenized assets mean for cross-asset monitoring
- How regulatory divergence can affect surveillance strategy and infrastructure
- The technology, data, and skills questions teams are assessing in 2026
Explore OneTick Trade and Market Surveillance
Learn more about KX’s OneTick Trade and Market Surveillance product.