AI in Financial Services Litigation: What’s Now and What’s Next

SmartBrief: What is the biggest impact AI has had on securities litigation?

Michelle Cooper: Overall, AI has not changed the elements of securities fraud, but it is changing what plaintiffs argue was “known,” how dense financial information is digested and interpreted by laypeople (aka juries), and what defendants should have understood before these cases reach litigation. Hindsight issues affect all practice areas. However, they are especially pertinent to securities litigation.

The biggest impact AI has had is eliminating the advantage of providing the public with cold-hard data. Just as with legal evidence, juries are becoming skeptical of numbers being thrown at them, unsure if voluminous information equates to truth. Securities cases once turned on who could process the most information in the least amount of time, present themselves as the more credible party based on the data, and ultimately, leverage confusion and fatigue as paths to success.

Now, AI facilitates the analysis of millions of documents, trading patterns, and disclosures, leveling the playing field for parties. Thus, the crux of the legal argument and jury presentation sits upon effective framing, not volume. Who can effectively translate complex data into a clear, credible narrative about intent, materiality, and loss causation?

SB: What are some of the biggest legal risks AI poses for financial services?

Cooper: The biggest legal risks involve misalignment between what companies say their AI does and what it actually does. This ties back to the credibility pitfall I mentioned earlier. In financial services, AI touches multiple facets, including research, trading, compliance, and forecasting.

If disclosures—which are often at the heart of securities fraud disputes—overstate AI controls or scope, underrepresent reliance on AI, or fail to properly vet or audit AI-based trading models or platforms relied on for data or research, those issues become ripe for legal claims. This is especially true following a market event that exposes these gaps.

Financial institutions are also being warned to remain mindful of potential antitrust violations when incorporating AI. As AI becomes more widely used, many people are producing similar calculations and breakdowns, leading to legal issues. A barrage of cases has flooded the courts involving real antitrust concerns: companies all using algorithmic pricing tools, potentially constituting a form of price-fixing.
Further, with the lingering corporate bias in jury pools, it is more important than ever to avoid credibility pitfalls. One such pitfall is over-precision in projections. As AI evolves, the programming appears to be increasingly accurate. This can build false confidence in AI outputs and the accuracy of projected company performance. If it deviates, plaintiffs will argue that management treated probabilistic tools as deterministic ones.

Another pitfall is inconsistency and explainability. If narratives do not align or executives cannot clearly articulate how the AI system works, these discrepancies can surface quickly and clearly in litigation, damaging credibility in front of a jury.

SB: Are there any emerging risks that you think are being overlooked?

Cooper: I think one of the primary risks is overreliance on AI. Courts are already cracking down on attorneys who blindly rely on AI, which,
although helpful in condensing information, is not always accurate. It is almost a type of “governance fatigue” where AI tools are being integrated and approved at such a rapid rate without necessary testing or due diligence.

Companies and law firms are neglecting a thorough evaluation of potential long-term effects and bypassing the need for established contingency plans if AI causes issues down the line. If something goes wrong, plaintiffs will frame that as a failure of oversight and reckless behavior, especially if there was not meaningful documentation of AI model validation, instead of just a technology issue.

SB: How are “AI-washing” claims redefining securities litigation?

Cooper: AI-washing, like “green-washing” with purported eco-friendly policies, is the latest fad in today’s corporate trends. It is reframing traditional misrepresentation theories.

Securities litigation is no longer about inflated earnings projections or executives making statements beyond mere puffery. Now, it involves companies touting AI-driven resources and integrated capabilities every chance they get—a dangerous slippery slope. 

The stakes are high in securities litigation because when companies label products as “AI-driven,” plaintiffs are going to ask: Was that glossy branding trying to distract us from what is really at issue here? Or was that an accurate representation of capability? Essentially, materiality debates on marketing language.

SB: In the next 12 to 18 months, what are going to be the hottest topics in securities litigation when it comes to AI?

Cooper: I believe the hot topics involving AI will revolve around adequate disclosure and controls for effective AI monitoring. Specifically, whether companies accurately describe AI governance, risk management practices, and internal testing. After a few high-profile enforcement actions or AI-driven stock drops, there will be a wave of rapid follow-on filings.

For trial teams involved in securities litigation, it will be critical to monitor how these cases play out in front of juries nationwide, to inform better-framed case presentations that align with credible (not glossy) legal theories.

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This article was originally published by SmartBrief; republished with permission.