By Sachin Mohan · AIM Media House
Goldman Sachs has spent decades automating its trading floors. Risk models, algorithmic execution, and quantitative strategies. Technology has always been central to how the bank competes. What is new is where the automation is pointing next.
Inward, into the compliance and surveillance functions that have historically resisted it. Bloomberg reported that Goldman Sachs is exploring the deployment of agentic AI tools for trading surveillance, mainly to look for suspicious signals or movements in the market. A representative for Goldman declined to comment.
But the report landed three weeks after Goldman's CIO Marco Argenti told CNBC something more specific.
The bank has spent six months co-developing autonomous AI agents with Anthropic, built on the Claude model, targeting trade accounting, client onboarding, and compliance, with employee surveillance explicitly named as a next step.
Many banks are already evaluating ways to integrate AI to save costs and improve efficiency. Currently, most trading surveillance is done using rule-based algorithms programmed to detect issues. When 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. "Think of it as a digital co-worker for many of the professions within the firm that are scaled, are complex and very process intensive," said Marco Argenti, Chief Information Officer, Goldman Sachs.
Implementing Agentic AI Unlike AI chatbots that simply supply information, agentic AI is designed to plan and take action autonomously. In a trading context, this means the software can decide what data to examine next, compare multiple signals, and escalate findings without constant human input.
It might monitor order flows, price movements, communications metadata, and historical behavior to assess whether activity aligns with normal patterns.
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