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AI on Wall Street mostly sits in operations, compliance and analysis, and some systems inform or automate investment-related actions. What could go wrong is a set of identified risks rather than a documented market event. A model can run on bad or stale data or be hard to inspect. A firm’s incentives can pull against its customers’ interests. Many firms can rely on the same models or vendors and react alike under stress, and automated responses can move faster than people can step in. Official reports and speeches treat these as risks to manage, not as events that have already happened.
What “AI on Wall Street” actually covers
The phrase bundles tasks with very different stakes. Back-office processing and compliance work is a long way from a system placing trades, and lumping them together makes both the benefits and the risks look larger or smaller than they are.
| Area | Examples named in official material | Role in decisions |
|---|---|---|
| Back office and compliance | Operational efficiency and regulatory compliance (Financial Stability Board, FSB); call centers and claims processing (then-SEC Chair Gary Gensler, June 2024) | Mainly operational; not primarily investment decisions |
| Analysis and prediction | Predictions about markets, loans and credit (Gensler, June 2024); advanced analytics (FSB) | Feeds human or automated decisions |
| Investment-related actions | Systems that inform or automate investment-related actions. In September 2026, SEC Commissioner Mark T. Uyeda said retail investors and large institutions alike were incorporating AI tools into investment decisions and operations. | Ranges from informing a person’s choice to automating the action. Official material does not state how many final decisions are made without human approval. |
Those statements establish the range of uses. They do not establish how many firms hand over final decisions, or how often a person reviews the output before it takes effect.
The upside
The FSB describes AI benefits across four areas:
- Operational efficiency
- Regulatory compliance
- More personalized financial products
- Advanced analytics
A 2024 FSB summary of an OECD-FSB roundtable reported that generative-AI use at regulated financial institutions then looked exploratory and centered mainly on operational efficiency. That is a picture of 2024, not a current survey of adoption.
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These gains are the reason firms adopt the tools. They do not remove the need to test systems before relying on them, manage conflicts of interest, or keep clear accountability for outcomes.
What could go wrong
The risks below fall into five groups. Some are about a single system failing. Others only matter when many firms are doing the same thing.
Model and data failure
An AI system is only as reliable as its inputs and its design. The FSB identifies bad, incomplete, biased or outdated data, along with opaque or poorly governed design, as routes to failure. A flawed model can produce poor analysis, operational disruption or adverse investment outcomes. When a model is hard to interpret, it is also harder for a firm’s risk staff and outside supervisors to challenge it. These are failure modes, not inevitable outcomes.
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An automated recommendation or customer interaction can serve a firm’s incentives rather than the customer’s interests. The SEC’s 2023 proposal addressed conflicts tied to predictive data analytics; its current status is covered in the regulatory section below.
The related concern is disclosure. In a March 18, 2024 statement, then-SEC Chair Gary Gensler said:
“In essence, they should say what they’re doing, and do what they’re saying.”
In the same statement he said:
“AI washing, whether it’s by financial intermediaries such as investment advisers and broker dealers, or by companies raising money from the public, that AI washing may violate the securities laws.”
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That is a regulator’s warning that misrepresenting how AI is used can carry legal exposure. It is not a complete description of securities law.
Correlated decisions and market stress
If many firms use common models, data sources or design choices, they may trade, lend or price in similar ways. Similarity matters most under stress. Automated strategies can respond within seconds, which could worsen a liquidity squeeze or market stress before people can intervene. The FSB treats this as a potential financial-stability vulnerability. It does not show that this has occurred, and the official reports cited here do not document an AI-driven flash crash.
Cybersecurity, fraud and disinformation
AI can widen the attack surface, especially where systems depend on large volumes of data and third-party services. Generative AI can also make fraud and market disinformation more convincing or cheaper to produce. Official material describes this exposure in general terms. It does not document a specific AI-generated market manipulation case.
Concentrated providers and outages
Many institutions depend on a small number of providers for specialized hardware, cloud infrastructure and pretrained models. Where substitutes are limited, a disruption at one provider can affect many firms at once. The FSB’s 2025 monitoring report says authorities face data gaps and a lack of standardized taxonomies as they track AI adoption and related risks, which limits their view of where these dependencies sit.
Pathways versus documented events
Most of the risks above are identified pathways. The table separates them from anything the official material describes as having occurred.
Rank #4
| Risk pathway | Where it is identified | Status in the official material |
|---|---|---|
| Bad, incomplete or opaque data and models leading to poor outcomes | FSB; Commissioner Caroline Crenshaw’s March 2025 remarks | Identified risk; no specific incident cited |
| Recommendations that favor the firm over the customer | SEC 2023 predictive-data-analytics proposal, withdrawn June 2025 | Concern identified; the proposal is not a current rule; no specific case cited |
| Misstatements about how AI is used (“AI-washing”) | Gensler statement, March 18, 2024 | Warning that it may violate securities laws; no specific case cited |
| Correlated behavior amplifying volatility or liquidity pressure | FSB | Potential vulnerability; no AI-driven flash crash documented |
| AI-enabled cyberattacks, fraud and disinformation | FSB; Crenshaw’s March 2025 remarks | Exposure identified; no verified AI-generated market manipulation case |
| Disruption at a concentrated provider affecting many firms | FSB; FSB 2025 monitoring report | Shared vulnerability identified; no specific provider failure cited |
What happens when AI makes investment decisions
The answer depends on how much human judgment stays in the loop and what the system is allowed to do. Official material does not provide a reliable rate of AI-directed investment decisions, so no general answer on frequency is possible. A person signing off on an output is also not the same as a person who can understand the system and stop it. The FSB’s and Crenshaw’s concerns point to five questions to ask about any given use:
- Can the firm explain why the system acted, and can an independent party audit it?
- Are the data reliable, current and appropriate for the decision?
- How quickly can a person spot a problem and override or halt the system?
- Are the firm’s incentives aligned with the customer’s interests?
- Does the system rely on a shared model, data source or concentrated vendor that other firms also use?
A use that performs poorly on several of these questions is where the risks above are most likely to apply. These are comparison axes drawn from the identified risks. The official material does not rank particular systems or firms.
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The 2023 predictive data analytics proposal
The SEC proposed rules in 2023 addressing conflicts of interest associated with predictive data analytics. The Commission withdrew those proposals in June 2025, said it did not intend to issue final rules on them, and said any future action would start with a new proposal. The withdrawn proposal is not a current adopted rule and should not be described as binding law. Existing securities-law obligations and other applicable rules may still matter. This article does not attempt a complete legal analysis of them.
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Questions from a commissioner
In March 2025, SEC Commissioner Caroline Crenshaw set out questions for regulators and market participants. They cover how to govern black-box systems, how to meet legal and fiduciary duties, what must be disclosed, how vulnerable investors are protected, and which systemic and volatility risks arise. Her remarks were a speech, and she stated that her views were her own and not necessarily those of the Commission. They are questions, not adopted SEC policy.
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What the evidence does and does not establish
- Established: the FSB and SEC officials identify material AI-related risks in data and model quality, conflicts of interest, correlated behavior, cyber exposure and provider concentration.
- Not established: a specific market crisis or trading loss caused by AI.
- Not established: how often AI directs investment decisions, or how many firms let it do so without human approval.
- Not established: realized financial losses caused by AI-directed decisions. No comparable figure appears in the official material.
The FSB’s November 14, 2024 report summary gives its stated conclusion:
“The rapid adoption of AI in finance means that authorities should address information gaps for monitoring, assess the adequacy of current policy frameworks and enhance supervisory and regulatory capabilities.”
The one large number in the SEC’s material is not about AI. In June 2024 remarks, Gensler cited $110 trillion as the scale of the capital markets the SEC oversees. It measures market size, not AI investment, adoption or losses.
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