Banks are testing a new type of artificial intelligence, like agentic AI, that does more than scan for keywords or follow preset rules. Instead of relying only on static alerts, some trading desks are beginning to use systems designed to reason through patterns in real time and flag conduct that may need human review.
Bloomberg detailed how Goldman Sachs and Deutsche Bank are exploring or deploying so-called “agentic” AI tools for trading surveillance. The goal is to strengthen oversight of orders and trades by using software agents that can analyse activity as it happens and identify patterns that could suggest misconduct.
Adaptive agents
Large banks use automated surveillance systems to monitor trading activity, systems that often rely on predefined rules: if a trade exceeds a certain size, deviates from a benchmark, or fits a known risk pattern, it triggers an alert. Compliance teams then review the case manually.
The challenge is scale and complexity. Modern markets generate huge volumes of data in asset classes, time zones, and trading venues. Static rules can generate large numbers of false positives, while more subtle forms of manipulation may not match known patterns.
According to Bloomberg, the newer agentic systems aim to go beyond that approach. Rather than simply matching trades against a checklist, the AI agents are designed to examine trading behaviour in multiple signals, compare it with historical activity, and detect unusual combinations of actions.
The tools are not described as replacing compliance officers. Instead, they appear to function as an additional layer of monitoring, surfacing cases that warrant closer human inspection.
Deutsche Bank’s work with Google Cloud
Bloomberg reported that Deutsche Bank is working with Google Cloud on developing AI agents that can monitor trading activity. The system is designed to review large sets of order and execution data and flag anomalies in near real time.
The bank has been expanding its AI initiatives over the past few years, and this surveillance effort reflects how financial institutions are applying generative and large language model technology beyond chat interfaces. In this context, the AI is not answering customer questions but analysing structured and unstructured data streams tied to trading behaviour. The AI agents can help identify “complex anomalies” in orders and trades. That suggests the system may look at relationships between trades, timing, market conditions, and trader history not single events in isolation.
Human compliance staff remain responsible for reviewing flagged cases and determining whether further action is required.
Goldman Sachs’ agentic AI strategy
Goldman Sachs is also exploring the use of agentic AI for surveillance, according to Bloomberg. The bank has invested heavily in AI in its trading and risk systems in recent years, and this effort appears to extend that work into compliance.
The focus, as described in the report, is on using AI agents that can operate with a degree of independence in scanning for misconduct indicators. The system may identify patterns that do not fit a clear rule but still stand out as unusual.
For regulators, the appeal is straightforward: earlier detection can reduce market harm and reputational risk. For banks, there is also an operational dimension. Compliance departments face pressure to handle large volumes of alerts while maintaining strict oversight standards. Tools that can reduce noise without lowering scrutiny are likely to attract attention.
Why “agentic AI” matters
The term “agentic AI” refers to systems that can take goal-directed actions not respond to prompts. In practice, that can mean the software is able to decide what data to examine next, compare multiple signals, and escalate findings without constant human input. In a trading context, that might involve monitoring order flows, price movements, communications metadata, and historical behaviour to assess whether activity aligns with normal patterns.
This does not mean the system makes disciplinary decisions on its own. Financial institutions operate under strict regulatory regimes, and accountability remains with human supervisors. The agent’s role is to identify and organise information more effectively than static systems can.
Part of a wider compliance shift
What appears new is the application of more advanced generative AI architectures to internal control functions.
Regulators in the US and Europe have encouraged firms to improve the monitoring of market abuse and manipulation. While rules do not mandate agentic AI, they do require firms to maintain effective systems and controls. If AI tools can help meet that standard, adoption is likely to grow.
At the same time, AI in compliance raises its own questions. Banks must ensure that models are explainable, that they do not introduce bias, and that they can withstand regulatory review. Model governance, data security, and audit trails remain central concerns.
What changes for the industry
If agentic surveillance tools prove effective, they could alter how compliance teams work. Instead of sorting through large volumes of simple alerts, staff may spend more time evaluating complex cases surfaced by AI agents.
That change would not remove the need for human judgement. It may, however, change where human effort is focused. In markets where speed and data volume continue to rise, the ability to analyse patterns in real time is becoming harder to achieve with rule-based systems alone.
(Photo by Markus Spiske)
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