In a time when financial markets are more interconnected than ever, one of the most urgent gaps in trade surveillance remains unresolved: Cross-Market Alerting. While many firms have matured their single-market surveillance frameworks, they still struggle to detect sophisticated behaviours that unfold across different instruments, venues, or asset classes. The result is a dangerous blind spot, one that regulators are becoming increasingly aware of, and one that firms can no longer afford to ignore. Modern advances in artificial intelligence (AI) and machine learning (ML) provide new opportunities to identify these hidden patterns.
What Is Cross-Market Alerting?
Cross-market alerting refers to the capability of a surveillance system to detect potentially abusive or manipulative activity that spans across multiple trading environments. This may include:
- Executing trades in one market to impact prices in another
- Coordinated trading of correlated instruments across venues
- Layering or spoofing in one product to affect another (e.g., futures vs. underlying equities)
- Wash trades executed across different platforms to disguise intent
Traditional surveillance systems, built around isolated data silos, often fail to connect these dots, either due to technical limitations or because compliance teams lack a unified view of trading behaviour.
Why It Matters Now
The market manipulation of today is rarely confined to a single venue. With algorithmic trading, high-frequency strategies, and global market access, abuse has grown in both complexity and scope. Regulators know this. Enforcement actions increasingly cite failures in firms’ ability to detect cross-market schemes.
Recent regulatory priorities, from MAR in Europe to the SEC’s emphasis on surveillance technology are making it clear: reactive compliance is no longer acceptable. Firms need to demonstrate that they can understand intent behind trading behaviour, and that often requires joining the dots across markets.
Key Challenges Firms Face
- Fragmented Data Architecture
Firms often operate with siloed systems for different desks, asset classes, or regions. Aggregating this data in a consistent and timely manner is difficult and expensive. - Lack of Normalized Data
Even if data is accessible, differences in time stamps, formats, identifiers, and venues create a barrier to meaningful cross-market correlation. - Legacy Technology
Older surveillance systems weren’t built with multi-market abuse in mind. Retroactively adding these capabilities is a complex and costly endeavour. - Alert Fatigue
When poorly implemented, cross-market alerts can increase noise rather than clarity, adding to the workload without improving risk detection.
At b-next, we believe the solution lies in configurable, scenario-based surveillance backed by a normalized, flexible data layer and AI-driven anomaly detection models. Cross-market alerting requires a system that can:
- Ingest and reconcile trading data across venues
- Link related instruments and behaviours (e.g., equity and derivatives)
- Correlate orders and executions across timeframes and venues
- Apply AI or rule-based detection models across the entire trading landscape
AI and ML are particularly powerful in cross-market surveillance because they can process the high volume and velocity of trading data that traditional systems struggle with. By applying advanced anomaly detection techniques, and predictive analytics, compliance teams can identify hidden correlations and spot manipulation patterns that would otherwise remain undetected. This shift enables firms to adapt detection scenarios dynamically moving from reactive compliance to proactive risk mitigation, capturing subtle multi-venue abuse strategies with greater accuracy.
Looking Ahead
Cross-market alerting will not be an optional capability for long. As regulators raise expectations and market manipulation techniques evolve, firms will need to adopt more integrated and intelligent AI/ML-powered surveillance systems. It’s not just about avoiding fines, it’s about protecting market integrity, detecting risks proactively, and staying ahead of manipulation strategies.
The question is no longer if firms will adopt cross-market alerting, it’s when, and how effectively.