Goldman Sachs and Deutsche Bank test agentic AI in trading

Banks are now deploying a new breed of artificial intelligence in their trading surveillance systems — one that doesn’t just scan for keywords or follow static rules, but actually reasons through real-time patterns to flag potentially suspicious conduct. According to a report by Bloomberg, major financial institutions like Goldman Sachs and Deutsche Bank are exploring or actively deploying so-called “agentic AI” tools to strengthen oversight of trading activity, using software agents that analyze behavior as it happens and identify anomalies that may warrant human review.

Traditional surveillance systems in large banks typically rely on predefined rules: if a trade exceeds a certain size, deviates from a benchmark, or matches a known risk pattern, an alert is triggered. Compliance teams then manually review each case. However, as modern markets generate massive volumes of data across multiple asset classes, time zones, and trading venues, these static systems often struggle with false positives and can miss more subtle forms of manipulation.

The newer agentic AI systems aim to go beyond this rule-based approach. Instead of simply matching trades against a checklist, these AI agents examine trading behavior across multiple signals, compare it with historical activity, and detect unusual combinations of actions. They are not designed to replace compliance officers, but rather to function as an additional layer of monitoring, surfacing cases that warrant closer human inspection.

Deutsche Bank, for example, is working with Google Cloud to develop AI agents that can monitor trading activity in near real time. The system reviews large sets of order and execution data to flag anomalies, looking at relationships between trades, timing, market conditions, and trader history — not just single events in isolation. Human compliance staff remain responsible for reviewing flagged cases and determining whether further action is required.

Goldman Sachs is also exploring the use of agentic AI for surveillance, focusing on AI agents that can operate with a degree of independence in scanning for misconduct indicators. These systems may identify patterns that do not fit a clear rule but still stand out as unusual.

The term “agentic AI” refers to systems that can take goal-directed actions, not just respond to prompts. In practice, this means the software can 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 behavior to assess whether activity aligns with normal patterns.

This development is part of a wider shift in compliance technology. 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, the use of 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.

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 judgment, but it may change where human effort is focused. In markets where speed and data volume continue to rise, the ability to analyze patterns in real time is becoming harder to achieve with rule-based systems alone.

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