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1 [1] (September 15, 2025)

handle is hein.crs/govetxp0001 and id is 1 raw text is: Congressionol Research Service Informing the Iegisl9tive debate since 1914 0 September 15, 2025 Artificial Intelligence in Capital Markets: Policy Issues Artificial intelligence (AI) has the potential to transform operations and regulation of capital markets. This In Focus lays out some background and policy implications relevant to congressional oversight and legislative activities. A Defint on in Capita Markets 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 Al in a notice of proposed rulemaking in June 2023 (discussed in more detail below) as a type of predictive data analytics- like technology, describing it as the capability of a machine to imitate intelligent human behavior. A Use in Captal Markets The scope and speed of Al 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 Al tools to obtain market share). Both capital markets industry participants and the SEC may find use for Al as shown below. Capital Markets Use Common Al usage in capital markets includes (1) investment management and execution, such as investment research, portfolio management, and trading; (2) client support, such as robo-adviser service, chatbots, and other forms of client engagement and underwriting; (3) regulatory compliance, such as anti-money laundering and counter terrorist financing reporting and other compliance processes; and (4) back-office functions, such as 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 Al 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 Al 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 Al within the agency in its AI Use Case Inventoryfor 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, and (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 Al use and requiring each agency to develop an Al strategy, share certain Al assets, and enable an Al-ready federal workforce. Seected Policy Issues While Al offers potential benefits associated with the applications discussed in the previous section, its use in capital markets also raises policy concerns. Below are examples of issues relating to Al use in capital markets that Congress may want to consider. Auditable and explainable capabilities. Advanced Al 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 Al analysis. Some financial regulatory authorities have developed Al tools (e.g., Project Noor) to gain more auditability into high-risk financial Al models. Accountability. The issue of accountability centers around the question of who bears responsibility when Al systems fail or cause harm. The first known case of an investor suing an Al developer over autonomous trading reportedly occurred in 2019. In that instance, the investor expected the Al to outperform the market and generate substantial returns. Instead, it incurred millions in losses, prompting the investor to seek remedy from the developer. Al-related information transparency and disclosure. Al washing-that is, false and misleading overstatements about Al use-could lead to failures to comply with SEC disclosure requirements. Specifically, certain exaggerated claims that overstate Al usage or Al-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 Al use.