Trade surveillance has never been more important or more difficult. As global markets accelerate, regulations tighten, and trading systems produce unprecedented volumes of data, surveillance teams are hitting their breaking point.
Compliance leaders across asset managers, broker dealers, and banks all share the same reality: the noise is overwhelming, the risk is growing, and legacy tools cannot keep up.
At Hakkoda, we work with some of the most sophisticated financial institutions in the world, and we consistently see the same theme: firms are not struggling because they lack alerts, they are struggling because they have too many of them.
This is why we built a cloud-native, AI powered Trade Alert Intelligence platform using Snowflake Cortex.
This blog is the first in a two-part series that will dive into the underlying industry problem, explain the how and why of AI’s emergence in the trade surveillance landscape, and finally walk you through how our solution transforms the way compliance teams work.
The Industry Is Drowning in Alerts
Trade surveillance systems were originally designed for a very different era of financial markets.
Their primary job was to monitor order execution behavior, detect potential market abuse, and generate alerts for review.
In theory, this workflow functions well. In practice, it no longer does. Here’s why.
1. Data Volumes Have Exploded
Modern OMS and EMS platforms generate millions of order events every day. When you multiply this across traders, desks, asset classes, venues, and regions, the scale becomes unmanageable.
When everything looks suspicious, nothing truly stands out.
2. Legacy Rule Engines Trigger Mostly False Positives
Traditional surveillance tools rely heavily on rule logic. Rules flag behavior based on thresholds, cancellations, order sizes, or timing patterns.
These rules are expected and required by regulators, but they lack context.
The result is that 60 to 90 percent of surveillance alerts are false positives.
Teams spend hours reviewing behavior that is normal for the trader, normal for the product, normal for the market condition, or already explained by historical patterns.
Compliance costs rise. Analyst fatigue increases. True risk remains buried in noise.
3. Surveillance Teams Are Overwhelmed
Analysts must manually triage each alert, prioritize them, and explain the outcome. Compliance managers then audit the auditors, and regulators audit the entire process again.
With staffing challenges and growing regulatory expectations, this model is becoming unsustainable.
Firms are essentially paying highly skilled people to sift through noise.
4. Regulatory Pressure Is Increasing
Across the SEC, FINRA, FCA, ESMA, and MAS, enforcement actions related to supervision and recordkeeping are rising. Regulators now expect:
- Robust surveillance
- Clear audit trails
- Transparency into why alerts were escalated
- Proof that false positives are actively being reduced
- Consistency across analyst decisions
Legacy tools were not designed to provide this level of transparency or control at modern scale.
The result is higher regulatory exposure and operational risk for firms across the industry.
Why AI Is the Future of Trade Surveillance
Artificial intelligence, particularly large language model based classification, is uniquely positioned to solve the alert overload problem. Not by replacing rule engines, but by augmenting them.
Trade surveillance needs context, and AI is built to understand that context.
AI Understands Behavior, Not Just Rules
AI can analyze:
- Historical patterns
- Trader specific behavior
- Market conditions
- Instrument level characteristics
- Relative order sizes
- Sentiment markers
- Multi variable relationships
This allows AI to infer whether a behavior is unusual, risky, or entirely benign.
AI Can Prioritize Alerts Based on Actual Risk
Instead of treating all alerts as equal, AI can classify each one as:
- High Risk
- Medium Risk
- Low Risk
This dramatically reduces analyst workload and directs attention to meaningful activity.
AI Provides Explainability That Regulators Demand
LLMs can generate readable and transparent explanations such as:
“This trader submitted an order that was six times larger than their typical size and rapidly canceled most of the volume within seconds. This behavior aligns with known layering patterns.”
This level of clarity is exactly what compliance teams need.
AI Learns From Analyst Feedback
Surveillance systems with machine learning can adapt over time. With a feedback loop, analysts can correct classifications with a single action, and the model learns from those decisions.
Legacy rule engines cannot improve in this way.
The Path Forward for AI Trade Surveillance
The challenges facing today’s surveillance teams aren’t the result of weak controls. They’re the result of tools that were never designed for the scale, speed, and complexity of modern markets. Alert volumes will continue to climb, regulatory expectations will continue to intensify, and compliance teams will continue to be stretched thin unless firms rethink how they identify and prioritize real risk.
AI represents a turning point. By enriching alerts with context, filtering out noise, and surfacing the true signals that matter, firms can finally shift their energy from processing alerts to preventing misconduct.
In part two of this series, we’ll break down exactly how a Snowflake-native, Cortex-powered surveillance solution works in practice, and how Hakkoda’s Trade Alert Intelligence platform helps institutions reduce false positives, speed investigations, and strengthen compliance outcomes across the board.
You can also learn more about our AI trade surveillance solution and take the first steps in your data modernization journey by talking to our team today.
