by Traverse Legal, reviewed by Enrico Schaefer - October 3, 2025 - Artificial Intelligence
We represent AI companies that provide market analysis and trading tools, as well as businesses that utilize these tools in the market. In fact, AI tools are becoming increasingly commonplace within the financial, fintech, and banking industries. Here is what an experienced attorney specializing in Artificial Intelligence would want you to know about this growing use of LLMs.
AI-driven trading is no longer just a tool for cutting-edge hedge funds or fintech startups— AI is reshaping how banks, broker-dealers, asset managers, and compliance teams respond to market events. When the Federal Reserve’s latest rate cut hit the news, AI models across Wall Street executed trades in seconds, sparking volatility and drawing immediate attention from regulators. Now, the SEC and DOJ are asking firms not just what happened in the market, but whether you can explain how your systems behaved and prove you had controls in place.
The Federal Reserve’s latest rate cut triggered a wave of high-speed trading across AI-driven systems. Those trades moved markets, increased volatility, and caught the attention of regulators. These sorts of market consequences will be a key trigger for regulatory compliance by government agencies. The reason they are becoming more common is because of cost involved. In order to do financial analysis, companies used to have to invest tremendous financial resources to develop algorithms, test algorithms, adjust algorithms, which worked on financial data. This is no longer true with artificial intelligence. AI models now work directly on data, often through an API, without requiring any coding or algorithmic development.
Here is how this works. These AI systems are easy to deploy and often act without human review. They parse central bank signals, predict direction, and execute positions in seconds. Execution speed creates value and opens legal exposure. Regulators ask what your model did and why, and if your algorithm triggers a surge in options activity or amplifies price swings, they may treat the outcome as intentional even if no human touched a keyboard.
The legal system and regulators are catching up. The use of AI in financial markets has become a focal point in how the SEC and DOJ investigate potential manipulation and fraud. We expect a flood of class actions to be filed against businesses using these systems in the coming months and years. We are encouraging our clients to audit their systems in anticipation of either regulatory compliance, litigation, or investigation.
Video: NBC News coverage of the Fed’s September 2025 rate cut — a trigger event for AI-driven trading responses now facing regulatory scrutiny.
Machine learning models respond to monetary policy faster than any human team. They parse Fed statements, inflation data, and rate changes in real time. Further, they often execute trades with no pause, no confirmation, and often no direct oversight. These systems are trained to move fast. Speed drives volume and captures spreads, but removes the judgment regulators expect from firms trading on material economic events.
Speed and opacity create legal risk. Regulators do not care if your model acted on its own. They want to know whether your firm can explain the behavior and justify the outcome. If a model overreacts to a rate cut and triggers price dislocation, you may face scrutiny even without intent. The more autonomous your system is, the more pressure you face to prove control.
Understanding how AI systems are making decisions is challenging, unlike an algorithm where logic can be traced through the code, the internal workings of AI-based decision making are murky at best. Algorithm compliance can be a challenge, explaining why trading decisions occurred is also a challenge that needs to be met up front. Many AI systems are developing platforms that seek to build compliance, audit, and reporting into the system itself.
SEC AI enforcement is moving quickly, with regulators treating model outputs as if a person made the decision. If an AI system triggers a sharp move after a rate cut, the output is no longer seen as neutral, with it becoming potential evidence. The SEC and DOJ are shifting from theoretical analysis to direct investigation. They expect firms to explain how AI decisions are made, especially during high-volatility periods.
AI-driven flash crashes, unusual trading volume, and synchronized price moves after macroeconomic events are drawing scrutiny. When these patterns follow a rate decision or inflation release, regulators act.
The burden of proof is changing, and firms must show their systems did not intentionally manipulate the market, even if no individual directed the trade. AI is no longer an excuse, as it is a factor in how legal exposure is assessed.
Traditional manipulation cases require proof of intent. A trader must knowingly place orders or move volume to distort prices. AI changes the equation. Algorithms trade automatically based on code and data. No human directs each move, but the system can still create abnormal swings that look manipulative. This shift raises the core issue: can regulators hold a firm liable even if no one intentionally caused the outcome? Increasingly, the answer is yes.
Regulators treat algorithmic behavior as legally significant. If an AI system moves prices in a way that appears manipulative, the outcome alone may trigger enforcement. Firms cannot rely on autonomy as a defense and must show how their systems behave and what controls exist to prevent distortion. The absence of intent may no longer eliminate liability, shifting the burden to governance.
Every firm deploying AI in financial markets must expect regulatory scrutiny after major policy events. The following questions form the core of your legal defense strategy.
If you cannot answer the questions with evidence, you will not get the benefit of the doubt.
AI litigation in financial markets is no longer speculative. Tools once used to gain a trading edge now create legal exposure in real time. Regulatory enforcement has shifted from reactive to proactive. Uncontrolled, undocumented, and unexplained systems already violate legal standards.
Founders and legal teams must treat AI as a regulated actor, not a neutral tool. Every line of code executing financial transactions carries legal weight. Signal parsing, trade selection, risk limits, and execution speed each function carries legal consequences.
The burden of proof has shifted. Regulators no longer need to show malicious intent. They only need to demonstrate harm, instability, or manipulation caused by your systems.
Firms in this space must move beyond compliance theory. They need tested model governance, audit-ready logs, and legal strategies built for algorithmic behavior.
If your model can move markets, your model belongs to the legal system. Align your operations now, or the SEC will impose its own terms.
Visit Traverse Legal to audit your AI systems, review your controls, and prepare for enforcement before it happens.
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As a founding partner of Traverse Legal, PLC, he has more than thirty years of experience as an attorney for both established companies and emerging start-ups. His extensive experience includes navigating technology law matters and complex litigation throughout the United States.
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This page has been written, edited, and reviewed by a team of legal writers following our comprehensive editorial guidelines. This page was approved by attorney Enrico Schaefer, who has more than 20 years of legal experience as a practicing Business, IP, and Technology Law litigation attorney.
