AI and machine learning in trade surveillance: a 2025 guide
This article explores the rising pressures, regulatory expectations, and how companies can use advanced technology to gain a strategic advantage.
As in many other industries, AI and machine learning are reshaping trade surveillance in 2025. This article explores the rising pressures, regulatory expectations, and how companies can use advanced technology to gain a strategic advantage.
Financial markets are evolving faster than compliance teams can adapt.
Trading volumes are higher, products become more complex, and manipulative behaviours cross asset classes and geographies.
Regulators in the EU, US, and APAC are raising expectations, not just for detecting market abuse, but for doing it with technology that can explain its decisions.
Firms still relying on static, rules-based setups can risk:
- Missing cross-market abuse patterns that span multiple venues and asset classes
- Generating excessive false positives from static alert logic
- Failing regulator scrutiny due to a lack of explainability
Surveillance challenges
Modern markets generate millions of trades, orders, quotes, and related communications each day.
Even the most finely tuned rules can overwhelm analysts with thousands of false positives.
This is compounded by manipulative strategies that span multiple venues or asset classes, as covered in our cross-market surveillance guide, and the growing use of crypto markets, decentralised finance platforms (DeFi), and off-channel communications.
The result is a threefold challenge for surveillance teams:
- Data overload: vast, fragmented datasets across multiple systems
- Evolving abuse tactics: increasingly sophisticated, cross-market strategies
- Regulatory pressure: heightened demands for model governance and human oversight
The opportunity AI brings
AI and ML are not just efficiency tools; they expand the capabilities of trade surveillance.
By learning from historical patterns and adapting to new behaviours, they can detect market abuse that rules alone miss, integrate communication analysis, and support investigators with clearer, faster insights.
Some other key benefits include:
- Advanced detection of subtle or novel manipulation patterns
- False positive reduction through context-aware alerting
- Multi-asset coverage across equities, derivatives, FX, fixed income, and crypto (Crypto Asset Surveillance & MiCA Guide)
- Audit-ready explainability with transparent decision factors
- Scalability for 24/7 global market coverage
Joe Biddle, UK Markets Director at Trapets, reinforces this point:
"Financial institutions' current controls are no longer adequate against AI-driven threats. This issue must be addressed, and innovation in AI for good should be integrated into future anti-financial crime strategies to combat these challenges effectively."
How to implement AI in trade surveillance
Laying the right foundation is critical for AI success. Surveillance systems must first consolidate and normalise all relevant data, such as trade, order, and communication records, into a single environment.
Enriching this with reference data, account hierarchies, and KYC/PEP profiles (see our KYC and PEP & RCA guides) connects market behaviour with client identity and risk.
A practical AI integration roadmap includes:
- Hybrid detection models – Combine regulatory rules with ML algorithms to capture both known and emerging abuse patterns.
- Explainability by design – Use interpretable models with audit trails and SHAP explanations to satisfy regulators.
- Automated triage – Rank alerts by true-positive probability to focus analyst attention.
- Continuous training – Incorporate case feedback and market changes into regular model updates.
As Sanja Gabler, Chief Revenue Officer at Trapets, notes:
"To keep pace, companies must prioritise investing in their teams - building and training specialists who are critical to navigating complex regulations and defining the technology needed to support compliance efforts."
Regulatory focus in 2025
Regulators across jurisdictions are converging on the expectation that surveillance must be both technologically advanced and transparent:
- EU – MAR & MiFID II: Require integrated AI + human oversight, with Article 16-compliant investigations.
- US – SEC, FINRA, CFTC: Focus on spoofing, layering, and inadequate surveillance tech.
- APAC – MAS, ASIC, HK SFC: Encourage AI adoption but demand model validation and governance logs.
For a deeper analysis of these requirements, read our article on global trade surveillance regulations.
Final word
In 2025, AI and ML form the foundation of effective trade surveillance.
Companies that adopt them now will detect evolving threats more effectively, satisfy regulators, and improve operational efficiency.
Those who delay risk reacting to problems rather than preventing them.