AI's Effect on Capital Markets

Published Date : 16/09/2025 

The Securities and Exchange Commission (SEC) has observed the growing use of AI technologies in capital markets, including machine learning and large language model (LLM) chatbots. This article explores the various applications of AI in the financial sector and the regulatory and policy challenges it presents. 

The term 'AI' has been defined in federal laws such as the National Artificial Intelligence Initiative Act of 2020 as 'a machine-based system that can make predictions, recommendations or decisions influencing real or virtual environments.' The U.S. capital markets regulator, the Securities and Exchange Commission (SEC), referred to AI in a notice of proposed rulemaking in June 2023 as a type of predictive data analytics-like technology, describing it as 'the capability of a machine to imitate intelligent human behavior.'

The scope and speed of AI adoption in the financial sector are dependent on both supply-side factors (e.g., technology enablers, data, and business model) and demand-side factors (e.g., revenue or productivity improvements and competitive pressure from peers that are implementing AI tools to obtain market share). Both capital markets industry participants and the SEC may find use for AI as shown below.

Capital Markets Use

Common AI usage in capital markets includes:

1. Investment Management and Execution : Investment research, portfolio management, and trading.

2. Client Support : Robo-adviser service, chatbots, and other forms of client engagement and underwriting.

3. Regulatory Compliance : Anti-money laundering and counter-terrorist financing reporting and other compliance processes.

4. Back-Office Functions : Internal productivity support and risk management functions.

For example, in its 2023 proposed rule, the SEC observed that some firms and investors in financial markets have used AI technologies, including machine learning and large language model (LLM)-based chatbots, 'to make investment decisions and communicate between firms and investors.' LLM is a subset of generative AI that is capable of generating responses to prompts in natural language format once the model has been trained on a large amount of text data. An LLM can have applications in capital markets, such as answering questions and generating computer code. Furthermore, the Financial Industry Regulatory Authority, a self-regulatory organization for broker-dealers under the oversight of the SEC, described some machine learning applications in the securities industry, such as grouping similar trades in a time series of trade events, exploring options pricing and hedging, monitoring large volumes of trading data, keyword extraction from legal documents, and market sentiment analysis.

Regulatory Use

The SEC reported 30 use cases of AI within the agency in its AI Use Case Inventory for 2024. Examples include:

1. Searching and Extracting Information : From certain securities filings.

2. Identifying Potentially Manipulative Trading Activities .

3. Enhancing the Review of Public Comments .

4. Improving Communication and Collaboration : Among the SEC workforce.

In 2025, the Office of Management and Budget issued Memorandum M-25-21, providing guidance to agencies (including the SEC) on accelerating AI use and requiring each agency to develop an AI strategy, share certain AI assets, and enable 'an AI-ready federal workforce.'

Selected Policy Issues

While AI offers potential benefits associated with the applications discussed in previous sections, its use in capital markets also raises policy concerns. Below are examples of issues relating to AI use in capital markets that Congress may want to consider.

Auditable and Explainable Capabilities : Advanced AI financial models can produce sophisticated analysis that often may not have outputs explainable to a human. This characteristic has led to concerns about human capability to review and flag potential mistakes and biases embedded in AI analysis. Some financial regulatory authorities have developed AI tools, such as Project Noor, to gain more auditability into high-risk financial AI models.

Accountability : The issue of accountability centers around the question of who bears responsibility when AI systems fail or cause harm. The first known case of an investor suing an AI developer over autonomous trading reportedly occurred in 2019. In that instance, the investor expected the AI to outperform the market and generate substantial returns. Instead, it incurred millions in losses, prompting the investor to seek remedy from the developer.

AI-related Information Transparency and Disclosure : 'AI washing'—that is, false and misleading overstatements about AI use—could lead to failures to comply with SEC disclosure requirements. Specifically, certain exaggerated claims that overstate AI usage or AI-related productivity gains may distort the assessments of the investment opportunities and lead to investor harm. The SEC initiated multiple enforcement actions against certain securities offerings and investment advisory services that appeared to have misled investors regarding AI use.

Concentration and Third-Party Dependency : The substantial costs and specialized expertise required to develop advanced AI models have resulted in a market dominated by a relatively small number of developers and data aggregators, creating concentration risks. This concentration could lead to operational vulnerabilities as disruptions at a few providers could have widespread consequences. Even when financial firms design their own models or rely on in-house data, these tools are typically hosted on third-party cloud providers. Such third-party risks expose participants to vulnerabilities associated with information access, model control, governance, and cybersecurity.

Market Correlation : A common reliance on similar AI models and training data within capital markets may amplify financial fragility. Some observers argue that herding effects—where individual investors make similar decisions based on signals from the same underlying models or data providers—could intensify the interconnectedness of the global financial system, thereby increasing the risk of financial instability.

Collusion : One academic paper indicates that AI systems could collude to fix prices and sideline human traders, potentially undermining market competition and market efficiency. One of its authors explained during an interview that even fairly simple AI algorithms could collude without being prompted, and they could have widespread effects. Others challenged the paper, arguing that AI’s effects on market efficiency are unclear.

Model Bias : While AI could overcome certain human biases in investment decision-making, it could also introduce and amplify AI bias derived from human programming instructions or training data deficiencies. Such bias could lead to AI systems favoring certain investors over others (e.g., providing more favorable terms or easier access to funding for certain investors based on race, ethnicity, or other characteristics) and potentially amplifying inequalities.

Data : Data is at the core of AI models. Data availability, reliability, infrastructure, security, and privacy are all sources of policy concerns. If an AI system is trained on limited, biased, and non-representative data, it could result in overgeneralization and inaccurate predictions, leading to significant financial and social consequences. 

Frequently Asked Questions (FAQS): 

Q: What is AI in the context of capital markets?

A: AI in capital markets refers to the use of machine-based systems that can make predictions, recommendations, or decisions influencing real or virtual environments. This includes technologies like machine learning and large language models used for investment management, client support, regulatory compliance, and back-office functions.

Q: What are some common applications of AI in capital markets?

A: Common applications of AI in capital markets include investment research, portfolio management, trading, robo-adviser services, chatbots, anti-money laundering and counter-terrorist financing reporting, and internal productivity support.

Q: How is the SEC using AI?

A: The SEC has reported 30 use cases of AI within the agency, including searching and extracting information from securities filings, identifying potentially manipulative trading activities, enhancing the review of public comments, and improving communication and collaboration among the SEC workforce.

Q: What are the policy concerns related to AI in capital markets?

A: Policy concerns related to AI in capital markets include auditable and explainable capabilities, accountability, AI-related information transparency and disclosure, concentration and third-party dependency, market correlation, collusion, model bias, and data reliability and privacy.

Q: What is 'AI washing'?

A: AI washing refers to false and misleading overstatements about AI use, which can lead to failures to comply with SEC disclosure requirements and distort the assessments of investment opportunities, potentially causing investor harm. 

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