Regulating AI Deception in Financial Markets: How the SEC Can Combat AI-Washing Through Aggressive Enforcement
Introduction
Artificial intelligence (AI) has led to significant improvements in efficiency and innovation across industries. AI helps firms automate tasks, analyze large amounts of data quickly, and generate information which saves time, reduces costs, and improves accuracy. Yet these developments are undermined by a troubling trend of companies misrepresenting or falsely advertising their AI capabilities through “AI-washing.” Competitive pressures have compelled firms to project an image of technological sophistication that is not always accurate. The consequences are far from trivial as investors, regulators, and consumers rely on accurate representations of technological integration to make informed decisions. When those representations are distorted, the very foundations of market trust begin to erode.
While AI-washing impacts most industries, this article focuses on the financial services sector, where the stakes are particularly high given its reliance on investor trust and market integrity. The discussion will first survey the broader regulatory landscape governing AI claims, highlighting the lack of tailored oversight. It will then analyze the Securities and Exchange Commission’s (SEC) emerging role in policing AI-washing, emphasizing enforcement actions that signal a growing intolerance for such practices.
This article contends that the SEC must take a far more aggressive stance against AI-washing by rigorously enforcing existing anti-fraud statutes, imposing strict transparency requirements on algorithmic claims, and developing targeted remedies to hold firms accountable. Without such intervention, AI-washing will continue to distort competition, mislead investors, and erode the foundational trust that underpins financial markets.
Ultimately, this article aims to achieve three objectives: (1) demonstrate how AI-washing distorts competition and erodes consumer trust; (2) map the fragmented regulatory response to these risks; and (3) propose a more assertive SEC enforcement framework to deter misleading AI claims. By bridging the gap between technological hype and regulatory reality, this analysis seeks to underscore the urgent need for transparency in an era where AI’s promise too often outstrips its actual use.
AI-Washing in the Financial Services Industry
The financial services industry’s embrace of AI has been met with both genuine innovation and a troubling pattern of exaggeration. AI-washing distorts the true extent of AI integration, misleading investors, regulators, and consumers alike. Understanding how AI-washing operates, where it is most prevalent, and the legal risks it poses is essential for maintaining market integrity in an era where technological legitimacy can make or break a firm’s reputation.
AI-washing encompasses a spectrum of misrepresentations, ranging from subtle embellishments to outright fabrications. One of the most pervasive tactics is the overstatement of AI capabilities in disclosures. For instance, a hedge fund might market its trading strategy as “AI-driven,” suggesting that sophisticated machine learning models are at work when, in reality, the system relies on rudimentary rule-based logic. Similarly, banks have been known to label basic chatbots as “AI-powered,” even when their functionality stems from preprogrammed decision trees rather than adaptive natural language processing. Such claims create an illusion of innovation that has the consequence of enticing clients to believe they are engaging with cutting-edge technology.
Additionally, firms may showcase back-tested results where AI appears to outperform human analysts while omitting real-world scenarios where the same models falter. This selective disclosure creates a misleading narrative of reliability that lures investors who assume AI-driven strategies are inherently superior. Perhaps most concerning, however, is the omission of AI’s inherent risks. AI large language models exhibit algorithmic bias, data vulnerabilities, and model drift which are seldom disclosed, even as regulators sharpen their focus on these very issues.
Another common form of AI-washing involves the rebranding of traditional analytics as AI. Regression models, statistical analyses, and even Excel-based automation tools are frequently repackaged under the AI banner, despite lacking the adaptive learning capabilities that define true machine learning. Firms have the tendency to capitalize on AI’s market appeal without investing in the underlying technology.
The reach of AI-washing extends across nearly every corner of the financial industry, with certain sectors proving particularly susceptible. In banking and lending, institutions frequently claim that AI enhances credit scoring, yet many still rely predominantly on traditional FICO models with only superficial algorithmic tweaks. The Consumer Financial Protection Bureau has raised alarms about “black box” lending algorithms that obscure decision-making processes, potentially violating fair lending laws. When AI is invoked as a justification for credit denials or pricing disparities, the lack of transparency can trigger regulatory scrutiny.
Asset and wealth management is another hotspot for AI-washing due to the emerging trend of robo-advisory services. Robo-advisors have traditionally used preset algorithms for financial planning and investment management with minimal human intervention. While some newer platforms incorporate machine learning for minor adjustments, most rely on traditional modern portfolio theory rather than true AI. Nevertheless, many robo-advisors still market themselves as “AI-powered” despite using simplistic automation and pre-set templates for portfolio allocation, which has made them a prime target for AI-washing scrutiny by the SEC.
The Federal Trade Commission has already begun penalizing financial technology and cybersecurity companies for falsely advertising AI-powered security features for real-time fraud detection services, demonstrating that regulators are willing to intervene when marketing departs from reality. The insurance sector is not immune from AI-washing either. Insurers may claim that AI revolutionizes underwriting, yet manual assessments still dominate the process. Should these so-called AI models produce discriminatory outcomes, such as biased pricing based on protected characteristics, legal exposure under fair lending and anti-bias statutes becomes a real threat.
General Regulatory Landscape of AI
The regulatory landscape of AI remains fragmented as federal agencies take a sector-specific approach to AI regulation while states like New York pioneer broader and stricter oversight, particularly in health care, financial services, and employment. Most states are eager to regulate AI across industries with over 1,000 AI regulatory bills being processed in state capitals nationwide since January 2025. The initial draft of the “One Big Beautiful Bill Act,” almost codified a five-to-ten-year moratorium on AI regulation by states, but the provision was omitted in the final version.
At the federal level, there is no comprehensive AI governance framework. Instead, agencies have relied on a patchwork of existing statutory authority to rein in deceptive or harmful AI applications. The SEC has been particularly active, scrutinizing misleading AI-washing in investment advisement and trading under its anti-fraud and anti-manipulation provisions in Sections 10b-5 and 10b-6 of the Securities Exchange Act of 1934. The previous SEC chair, Gary Gensler, repeatedly warned that AI-driven conflicts could violate fiduciary duties (e.g., broker-dealers optimizing for their own interests over clients).
Meanwhile, the Consumer Financial Protection Bureau has targeted firms engaging in algorithmic bias in lending, invoking the Equal Credit Opportunity Act to challenge black-box credit models that disproportionately deny minority applicants. The FTC has taken an aggressive stance under Section 5 of the FTC Act, penalizing firms for exaggerating AI capabilities or deploying manipulative algorithms.
New York has emerged as a recognized leader in state-level AI regulation as its Department of Financial Services imposed strict model governance requirements for insurers and banks using AI, mandating bias testing, documentation, and executive accountability. More notably, New York City Local Law 144, which is the first of its kind in the U.S., requires independent audits of AI-driven hiring tools for racial and gender bias, with steep penalties for noncompliance. New York State is also leading the charge to establish licensing requirements for health-related AI applications to prevent AI systems from impersonating licensed professionals and requiring safety audits for medical chatbots.
The lack of federal preemption means companies must navigate a patchwork of standards set by federal agencies and state legislatures while also bracing for more stringent proposals on the horizon. The legislative frenzy at the state level reflects Congress’s failure to establish federal AI standards, leaving individual states to fill the regulatory void. Experts warn that the commerce clause was designed to prevent this type of decentralized and segmented approach to interstate commerce: “Fifty different AI regulatory regimes will undermine America’s ability to compete with China and other adversaries in the global AI race.” Many legal experts advocate for federal preemption legislation to create uniform national standards. Even so, there is a risk that a federal preemption legislation would unduly constrain the next wave of technological innovation or inadequately mitigate its potential harms.
While state and federal agencies grapple with AI’s challenges in a fragmented landscape, the SEC is uniquely positioned to lead the charge against AI-washing. By leveraging its well-established authority under the Exchange Act and Investment Advisers Act, the commission can demand verifiable proof of AI capabilities, penalize misleading omissions, and dismantle the veneer of technological sophistication that fuels investor deception. For now, companies must navigate a complex patchwork of state regulations and agency guidance to face AI’s novel challenges.
The SEC’s Regulation of AI
Section 10b-5 of the Exchange Act prohibits deceptive practices in connection with the purchase or sale of securities, including false statements, misleading omissions, and any scheme that would defraud investors. Central to this rule is the concept of materiality, which determines whether a misrepresentation or omission is legally significant enough to constitute fraud.
The U.S. Supreme Court has articulated the materiality standard in landmark cases, such as TSC Industries v. Northway and Basic Inc. v. Levinson, that a fact is considered material if there is “a substantial likelihood that a reasonable investor would view it as significantly altering the total mix of available information” when making an investment decision. In simpler terms, material information is anything important enough that its disclosure or concealment could influence an investor’s judgment.
Materiality is not merely a numerical threshold as it involves both quantitative and qualitative considerations. For instance, even a relatively small financial misstatement could be material if it conceals a larger pattern of fraud or misleads investors about a company’s true financial health. Conversely, minor inaccuracies, such as inconsequential rounding errors, may be deemed immaterial if they would not meaningfully impact an investor’s decision.
When assessing materiality in cases involving uncertain or speculative information, courts apply the “probability-magnitude” test from Basic v. Levinson. This test weighs both the likelihood of the event occurring and the magnitude of its impact if it does. For example, undisclosed merger discussions may be material if a deal is highly probable and would significantly affect stock prices, whereas vague, early-stage talks might not meet the threshold.
Examples of material information include false financial statements, undisclosed executive misconduct, major litigation risks, or critical developments in a company’s business (e.g., the failure of a key drug trial for a pharmaceutical firm). On the other hand, trivial errors, such as inconsequential typos in public filings, generally do not meet the materiality bar.
The materiality standard plays a crucial role in both SEC enforcement actions and private securities litigation. The SEC uses it to determine whether to pursue fraud charges, while private plaintiffs must prove materiality to succeed in lawsuits under Rule 10b-5. Companies, in turn, often defend against allegations by arguing that the disputed information was immaterial to investors.
The SEC has explicitly treated AI-related disclosures as per se material in many contexts, recognizing that AI is not merely a technological trend but a transformative force capable of reshaping business models and investor expectations. The SEC has demonstrated through both policy statements and concrete enforcement actions that AI represents a disruptive force demanding proactive regulatory oversight, particularly when broker-dealer firms exaggerate or falsify AI capabilities to attract investors. In fact, in a February 2024 speech before the Yale law faculty, Chairman Gensler warned that AI could become the next frontier of financial fraud and addressed the risks associated with AI. He cautioned reporting companies to avoid making boilerplate AI disclosures that are not particularized to their firm.
While the SEC has yet to establish comprehensive AI-specific regulations, its enforcement strategy has coalesced around two principal concerns: whether statements regarding AI functionality in corporate disclosures are materially misleading, and whether such disclosures that induced investors to purchase or sell securities were based on either the materially fraudulent information, deceptive practices, untrue statements, or omissions of material facts.
In recent years, the materiality of AI-related claims has incurred significant SEC scrutiny. When corporations assert in Form 10-K filings, prospectuses, or earnings calls that their products are “AI-powered” or that algorithms “fundamentally transform” investment strategies, these representations, if unsubstantiated, can significantly impact market behavior.
For example, in the case of In re Destiny Robotics Corp., the SEC alleged that the company made false and misleading statements about its AI capabilities, claiming to develop a sophisticated humanoid AI robot that was not realistically achievable within the stated timeline. The SEC also noted the failure to disclose material conflicts of interest and the misuse of investor funds contributed to the misleading nature of the disclosures.
The SEC also examines whether companies can substantiate their claims about AI capabilities. In In re Global Predictions, Inc., the SEC found that the company made unsubstantiated performance claims about its AI capabilities and failed to disclose material conflicts of interest, which rendered its disclosures misleading. Likewise, in In re Delphia (USA) Inc., the firm was found to have falsely claimed that its AI used client data to provide an investing advantage, despite admitting during an SEC examination that it had not developed such capabilities.
An example of the SEC applying the “total mix” materiality standard is in SEC v. Morgan Keegan & Co. where the court highlighted the importance of evaluating the totality of information available to a reasonable investor. Additionally, the SEC requires companies to ensure that their disclosures are not only accurate but also clear and complete, as misleading omissions or inaccuracies can violate securities laws, such as Rule 10b-5 under the Exchange Act.
The SEC’s framework for policing AI claims finds its doctrinal roots in earlier enforcement actions targeting environmental, social, and governance misrepresentations, particularly the 2021 case, In re BNY Mellon Investment Adviser. This landmark action saw the firm penalized $1.5 million for materially misleading statements about its ESG review process for mutual funds. The SEC’s order revealed that while BNY Mellon marketed certain funds as undergoing rigorous ESG quality reviews, numerous holdings lacked any such documented analysis. This discrepancy between advertised principles and operational reality established a critical precedent: when technological or methodological claims form the basis of investment products, their accuracy becomes material to investors under Section 206(4) of the Investment Advisers Act.
The administrative proceeding emphasized that BNY Mellon’s disclosures created an “overall misleading impression” despite containing no outright false statistics, a standard applied to AI claims where firms imply algorithmic sophistication beyond their actual capabilities. In 2023, the SEC’s Division of Corporation Finance issued comment letters to several financial services firms questioning vague claims about “proprietary AI trading models,” demanding specific disclosures about the technology’s actual role in investment decisions.
The BNY Mellon case assumes particular significance when analyzing AI-washing because it demonstrates the SEC’s willingness to penalize procedural misrepresentations, not just quantitative inaccuracies. Much like funds claiming nonexistent ESG screens, firms asserting AI capabilities they cannot substantiate risk similar liability.
This pattern of enforcement finds even starker expression in the Vale S.A. litigation. Here, the SEC’s 2023 complaint alleged the mining company knowingly misrepresented the safety of its dams prior to the catastrophic Brumadinho collapse that killed 270 people. While not an environmental, social or governance or AI case, Vale S.A.’s technological misrepresentations, particularly false claims about its proprietary monitoring systems, illustrate how materially investors rely on assertions of technological reliability. The SEC’s theory of liability turned on Vale S.A.’s repeated public assurances about its “Factor 1.0” safety protocol, which internal documents revealed to be largely theoretical. This dichotomy between marketed technological prowess and operational reality mirrors precisely the concerns surrounding AI-washing; firms may claim advanced machine learning capabilities while actually relying on rudimentary decision trees.
What makes these cases particularly instructive for AI regulation is their demonstration of the SEC’s approach to materiality assessments in technological claims. The commission’s litigation against Vale S.A. emphasized that even qualitative statements about systems and processes can violate Exchange Act Sections 10(b) and 13(a) if misleading. Similarly, BNY Mellon showed that disclosures need not be explicitly fraudulent to violate anti-fraud provisions; the omission of material facts about purported review processes was sufficient.
These enforcement actions reveal a critical truth: the SEC already possesses the legal tools to combat AI-washing. The Commission’s next step must be to systematize this approach by treating unsubstantiated AI claims as per se material misrepresentations, mandating algorithmic audit trails, and pursuing structural remedies like “algorithmic disgorgement” to deter misconduct at its source.
The SEC’s continued focus on AI-washing suggests these precedents built on existing Exchange Act regulatory framework will be invoked aggressively. Enforcement actions will target both outright fabrications and the more subtle omission of material facts about algorithmic limitations. Notably, the SEC’s Division of Examinations has incorporated AI-washing into its 2024 examination priorities, with particular focus on registered investment advisors because of a 2023 examination sweep that identified numerous advisors making unsubstantiated claims about AI-driven portfolio management.
Most recently, the SEC released its 2025 examination priorities that further expanded SEC oversight of AI. In addition to reviewing registrant representations regarding their AI capabilities or AI use for accuracy, the SEC will also assess whether firms have implemented adequate policies and procedures to monitor or supervise their use of AI. Broker-dealer firms must have a monitoring and supervisory program that encompasses back-office operations, anti-money laundering, trading functions, and fraud detection and prevention, as applicable. Reviews will also consider firm integration of regulatory technology to automate internal processes and optimize efficiencies. Significantly, the SEC indicated it would be examining how registrants protect against loss or misuse of client records and information that may occur from the use of third-party AI models and tools.
The challenge for both regulators and market participants lies in distinguishing between permissible business optimism and materially deceptive AI claims, especially given the technology’s rapid evolution. As the regulatory landscape matures, the SEC will likely continue developing its bifurcated approach of treating materially false AI claims as straightforward violations of existing anti-fraud provisions, while simultaneously working through rulemaking processes to establish more specific disclosure requirements for AI applications in financial services.
The SEC’s Evolving Stance on Regulating AI
The formation of specialized working groups within the SEC’s Division of Enforcement dedicated to emerging technologies initially signaled a coming wave of aggressive oversight. However, with the change in leadership at the Commission, the regulatory posture toward AI claims is likely to evolve in ways that reflect broader shifts in enforcement priorities. While the structural mechanisms for scrutiny remain in place, including the SEC’s specialized units and examination focus areas, the intensity of enforcement may moderate, particularly where AI representations fall into ambiguous or aspirational territory rather than outright deception.
That said, the underlying legal principles governing material misstatements have not changed. Even under a less hawkish administration, the SEC’s foundational mandate to police fraudulent and misleading disclosures ensures that AI-washing remains a compliance risk, particularly in high-stakes areas such as investment product performance, algorithmic reliability, and conflicts of interest in automated recommendations. The commission’s past enforcement actions demonstrate that once a framework for scrutiny is established, it creates a precedent that subsequent administrations can build upon even if the immediate pace of enforcement slows.
For market participants, this means that while the immediate regulatory pressure may not escalate as rapidly as previously anticipated. Even so, the fundamental expectation of accuracy and substantiation in AI-related claims persists. Firms that overpromise on capabilities, whether in marketing materials, regulatory filings, or investor communications, still risk enforcement under Rule 10b-5 or the Investment Advisers Act’s anti-fraud provisions. The key distinction may lie in the SEC’s threshold for action: where egregious cases of AI-washing once seemed likely to draw swift penalties, the new leadership may prioritize clearer evidence of investor harm before pursuing enforcement.
Yet, even in a less aggressive enforcement climate, market forces and private litigation could fill some of the gaps. Shareholder suits, state-level consumer protection actions, and private lawsuits have already begun targeting firms for AI-related misrepresentations, suggesting that even if the SEC’s own enforcement becomes more measured, the legal exposure for exaggerated claims remains; securities class actions targeting alleged AI misrepresentations have increased by 100% between 2023 and 2024 with no signs of abating through 2025.
A March 2025 ruling in the Southern District of New York found that a mobile health care provider, DocGo Inc., misled investors regarding its “proprietary central AI system” that purportedly managed complex logistics operations. Plaintiffs demonstrated the company falsely represented its CEO held a master’s degree in computational learning theory, a credential directly tied to the executive’s purported AI expertise in corporate communications. The court denied DocGo’s motion to dismiss, as it rejected DocGo’s arguments that such educational misrepresentations should be considered immaterial as a matter of law. The court distinguished prior cases involving similar statements by emphasizing the unique context of AI-focused companies. Most significantly, the ruling confirmed that in the AI sector, executive credentials directly tied to technological capabilities may constitute material information, a departure from traditional materiality analyses in other industries.
The case underscores how securities law principles and jurisprudence are adapting to the AI era. What might be considered puffery or immaterial detail in conventional contexts takes on new significance when connected to AI claims, reflecting both market expectations and the technology’s transformative potential. As companies increasingly highlight AI capabilities to attract investment, courts appear willing to hold them accountable for representations that reasonable investors would consider fundamental to their decision-making processes.
Proposed Framework for SEC AI Regulation
Notwithstanding the existence of the aforementioned investor remedies under securities laws, the regulatory landscape for AI is inadequate to effectively govern AI’s rapid evolution. The decentralized and fragmented approach creates gaps that bad actors can exploit while leaving consumers and investors vulnerable to harms ranging from algorithmic bias to AI misrepresentations to outright fraud. Similarly, the regulatory process itself has become deeply politicized, creating a cyclical pattern of aggressive rulemaking followed by sweeping rollbacks, an unstable framework that undermines effective governance. As such, the SEC stands uniquely positioned to lead the regulatory response by wielding its existing Exchange Act framework in AI regulation.
The SEC should continue applying its materiality standard to AI-related claims, treating unsubstantiated or exaggerated assertions as actionable if a reasonable investor would consider them significant in making decisions. This includes representations about AI-driven investment performance, risk management capabilities, or operational reliability. Firms unable to produce verifiable evidence supporting their AI claims upon request should face scrutiny under Rule 10b-5 and Section 17(a) of the Securities Act for materially misleading statements or omissions.
Additionally, the SEC should explicitly clarify through enforcement actions and guidance that omissions of AI limitations (e.g., known biases, failure rates, or reliance on unverified data) can constitute deceptive “half-truths” under Omnicare v. Laborers District Council. Boilerplate disclaimers should not shield firms from liability if their overall messaging overstates AI capabilities. Additionally, the use of intentionally non-transparent algorithms to obscure flawed logic may violate the “scheme liability” prong of Rule 10b-5, as reinforced by Lorenzo v. SEC.
For AI tools that have the potential to manipulate market activity, the SEC should aggressively apply Rule 10b-6 anti-manipulation provisions. Firms deploying AI-driven trading strategies must maintain real-time decision logs to demonstrate the absence of deceptive intent. Likewise, AI strategies that directly influence securities pricing, liquidity provisions, or order routing for consolidated audit trails (e.g., algorithmic trading platforms, predictive analytics models, and automated execution systems) should be aggressively scrutinized by the SEC. The SEC’s enforcement should focus particularly on vague AI strategies where the decision-making process cannot be easily traced or understood by market participants or regulators.
At the core of this enhanced oversight should be stringent transparency requirements mandating that firms maintain comprehensive, time-stamped audit trails of all AI-driven market activities. These records must document not only the final trading decisions but also the complete decision-making chain, including all data inputs feeding the AI models, the weighting given to various market signals, and any human interventions or overrides. Special attention should be paid to large transactions, with detailed explanations required for any AI-generated orders exceeding 0.5% of a security’s average daily volume.
The SEC must also presume that, on its face, vague AI trading strategies are inherently susceptible to manipulation. This would appropriately shift the burden to firms deploying such algorithms to demonstrate their systems’ compliance with market integrity rules. Firms claiming proprietary protection for their AI models should be required to provide at minimum: (1) third-party validation of their systems’ decision logic; (2) statistical analyses demonstrating the absence of manipulative patterns; and (3) clear documentation of all training data and model parameters that could influence market behavior.
This approach recognizes that while AI can enhance market efficiency, its potential for creating informational asymmetries and hidden manipulative patterns requires proactive safeguards. By combining rigorous transparency mandates with a presumption of scrutiny for opaque AI systems, the SEC can maintain market integrity without stifling responsible innovation. The goal is not to restrict technological advancement but to ensure that as AI becomes increasingly embedded in market infrastructure, it serves to enhance rather than undermine fair and transparent price discovery.
The transformative potential of AI in investment advisory services demands equally transformative regulatory oversight. Under Section 206 of the Investment Advisers Act, the SEC must rigorously enforce fiduciary standards for AI-powered advisory platforms, recognizing that the complexity and opacity of these systems create novel risks that traditional compliance frameworks may fail to address.
At the core of this enhanced oversight should be strict prohibitions against misrepresenting AI advisory capabilities. Advisory firms must clearly disclose the actual role and limitations of their AI tools, and whether they serve as supplemental analytical aids or make autonomous portfolio decisions. The SEC should scrutinize any claims of “AI-driven outperformance” with particular skepticism, requiring firms to produce auditable performance data that substantiates such assertions. Equally critical is the need to expose and eliminate conflicts of interest inherent in proprietary AI models, especially when algorithms disproportionately recommend in-house products or services without adequate disclosure.
To ensure meaningful accountability, the SEC should develop a specialized enforcement toolkit for AI-related advisory violations. Traditional monetary penalties may prove insufficient; instead, the commission should impose structural remedies tailored to algorithmic misconduct. This could include “algorithmic disgorgement” that would require firms to forfeit profits generated by misleading AI tools during periods of non-compliance. For systematically defective models, the SEC should mandate operational suspensions, compelling firms to recalibrate their AI systems under regulatory supervision before returning to market. Such measures would address misconduct at its source rather than merely punishing its symptoms.
Complementing these efforts, the SEC should leverage and expand its whistleblower program under Dodd-Frank Section 922 to uncover AI-related fiduciary breaches. Given the technical complexity of detecting algorithmic misconduct, insiders often serve as critical sources of information. Enhanced protections and incentives for employees who expose AI fraud could help surface violations that might otherwise remain hidden in proprietary code. By updating enforcement priorities, developing AI-specific remedies, and empowering whistleblowers, the SEC can ensure that the rise of machine-driven advice does not come at the expense of investor protection.
Overall, this framework positions the SEC to proactively regulate AI while maintaining market integrity, ensuring that innovation does not come at the expense of investor protection. By rigorously enforcing existing statutes with AI-specific interpretations, the SEC can deter misconduct without stifling responsible technological advancement.
Conclusion
AI has undeniably transformed the financial services industry. Yet as this article demonstrates, the sector’s rapid adoption of AI has been accompanied by a dangerous proliferation of exaggerated claims and technological misrepresentations. AI-washing distorts market competition and erodes the foundation of investor trust. The consequences extend beyond reputational harm, as AI-washing undermines fair pricing, enables informational asymmetries, and threatens systemic confidence in financial markets.
Our analysis reveals three critical realities. First, the regulatory landscape remains fragmented, with state and federal agencies applying inconsistent standards to AI claims. Second, the SEC has begun charting a more assertive path through targeted enforcement actions that treat material AI misstatements as violations of existing anti-fraud provisions. Third, private litigation is emerging as a powerful complement to regulatory oversight, with securities class actions related to AI misrepresentations doubling in recent years.
The proposed regulatory framework offers a pragmatic solution. Rather than await comprehensive AI legislation, the SEC should aggressively reinterpret its existing authority under the Exchange Act and Investment Advisers Act. This includes (1) applying heightened materiality scrutiny to AI performance claims, (2) mandating algorithmic transparency through immutable audit trails, (3) developing AI-specific remedies like “algorithmic disgorgement,” and (4) expanding whistleblower protections to uncover hidden misconduct. Such measures would preserve market integrity without stifling innovation.
As AI’s role in finance grows, so too must regulatory vigilance. The SEC’s mandate has always been to ensure that technological progress serves, rather than subverts, fair and efficient markets. By holding firms accountable for the accuracy of their AI claims, the commission can bridge the gap between AI’s promise and its practice.
In conclusion, the rise of AI in finance demands a regulatory response as sophisticated as the technology itself. The SEC, armed with existing anti-fraud authority and a growing body of AI-washing precedents, must act decisively to police false claims, compel transparency, and penalize firms that exploit the hype surrounding AI. Only then can the promise of AI be realized without sacrificing the integrity of financial markets.
