Technological Transformation via AI & Behavioural Analytics in Trade Surveillance

The accelerating digitisation of global markets is forcing financial institutions to rethink how they monitor trading activity, detect anomalies, and manage compliance risks. Traditional, rules-based surveillance may no longer keep pace with complex products, cross-venue strategies, and rising data volumes. Artificial intelligence (AI), machine learning (ML), behavioural analytics, and real-time data are reshaping the surveillance function, shifting it from static rule checks to dynamic, intelligence-led oversight.

This article explores how these capabilities could be leveraged to reduce noise, sharpen detection, and elevate the role of human analysts, while aligning with growing expectations for transparency and explainability.

AI & ML Integration: Smarter Prioritisation, Not Instant Simplicity

Financial markets generate staggering volumes of orders, trades, quotes, and communications. Legacy surveillance engines, often batch based and rules driven, struggle to keep up, frequently producing large numbers of alerts that require manual review. Introducing AI and ML does not automatically eliminate this burden; instead, it changes how it is managed.

By analysing high volume, multi-source data in real time, AI enabled systems can identify subtle behavioural signals that static rules often miss. Rather than simply firing more alerts, modern platforms aim to prioritise alerts by materiality and risk relevance. The goal is not to replace rules but to enhance them, adding context, ranking, and correlation so that analysts focus first on the cases most likely to require action.

Effective deployment therefore combines algorithms with workflow redesign, calibration discipline, and analyst feedback. When implemented in this way, AI and ML can help organisations move from high-volume, low-value alerting toward more targeted, context-rich surveillance outcomes.

From Rules to Adaptive, Behaviour-Based Surveillance

Behavioural analytics and machine learning help address this gap by monitoring activity patterns as they develop and comparing them to relevant benchmarks. These benchmarks are not fixed; they must evolve as traders change strategies, market conditions shift, and new products are introduced. Successful programmes recognise that behavioural baselines require continual refinement rather than one-time configuration.

Modern surveillance therefore combines several complementary lenses:

Combined, these perspectives can deliver more focused alerts and richer investigative context, while acknowledging that ongoing tuning and governance remain essential parts of the process.

Continuous Monitoring Where It Adds Value

The shift toward continuous surveillance does not imply that every form of monitoring must occur in real time. Certain behaviours, such as intraday spoofing or momentum ignition, benefit from immediate detection. Others, including patterns that emerge across multiple days or products, are better analysed through post-trade aggregation.

In practice, modern surveillance architectures blend both real-time signals and longer-term analytics. This balanced approach can allow firms to respond quickly where needed, while still building the broader evidentiary picture required for thorough robust investigations.

Data Quality: The Critical Enabler

Even the most advanced analytics are only as strong as the data underpinning them. Effective AI-driven surveillance depends on accurate order and trade records, consistent identifiers, aligned timestamps, and well-governed reference data.

Consequently, many organisations find that improving data foundations delivers as much benefit as deploying new models. Surveillance transformation must therefore be treated as a data programme as much as a technology programme.

By integrating front-office systems, market data, communications capture, and trade repositories into a coherent, well-governed information layer, institutions create the conditions necessary for advanced analytics to succeed and deliver their full value.

Rising Expectations for Transparency and Explainability

Regulators are not prescribing specific technologies, but they are raising expectations around effectiveness, governance, and demonstrable outcomes. Supervisors want clear, auditable answers to fundamental questions: Why did this alert trigger? What data and features contributed? How is the model monitored and controlled over time?

Whether a programme relies on rules, machine learning, or a combination of both, it must be supported by strong governance, covering model validation, performance monitoring, feature lineage, and change control.

Ultimately, the aim is not simply to deploy AI but to ensure surveillance processes remain robust, defensible, and adaptable as markets evolve.

The Human + AI Partnership

As surveillance becomes more sophisticated, the role of human experts becomes more, not less, important. AI augments surveillance professionals by shifting effort from repetitive triage toward higher-value analysis. At the same time, it introduces new responsibilities around model oversight and data stewardship.

In an effective operating model:

• Analysts spend less time on mechanical review and more on investigative intelligence, supported by richer signals and automatically assembled case materials.
• Data scientists and surveillance specialists collaborate to design features that reflect genuine trading behaviour and market microstructure.
• Compliance teams gain clearer dashboards, stronger evidence trails, and audit-ready narratives.
• Ongoing feedback from investigators helps refine models and keep them aligned with real-world risk.

In short, the partnership requires investment in training and governance, but it ultimately strengthens both detection capability and organisational resilience.

Conclusion

AI, behavioural analytics, and real-time data are reshaping trade surveillance. By combining behavioural signals, historical context, and live market conditions, firms can better detect nuanced manipulation, prioritise material risks, and strengthen governance.

The future of surveillance is a partnership: the speed and scalability of technology working alongside the judgement and experience of human analysts. Institutions that embrace this balanced model will be better equipped to operate with transparency, resilience, and confidence in today’s fast-moving markets.