Artificial intelligence is no longer a speculative force in class action litigation — it is a present and accelerating reality. From AI-powered fraud attempts in settlement programs to novel theories of algorithmic liability, courts and counsel alike are confronting unprecedented challenges.
At the same time, AI is becoming a strategic asset in the hands of skilled litigators, reshaping everything from class certification strategy to claims analysis — and now, the nuts and bolts of settlement administration.
This article explores the most pressing developments in how AI is transforming class actions today: as a source of corporate liability, as a driver of settlement fraud, and as a tool for both plaintiffs and defendants navigating an evolving litigation landscape.
Artificial intelligence is no longer a speculative force in class action litigation — it is a present and accelerating reality. From AI-powered fraud attempts in settlement programs to novel theories of algorithmic liability, courts and counsel alike are confronting unprecedented challenges.
At the same time, AI is becoming a strategic asset in the hands of skilled litigators, reshaping everything from class certification strategy to claims analysis — and now, the nuts and bolts of settlement administration.
This article explores the most pressing developments in how AI is transforming class actions today: as a source of corporate liability, as a driver of settlement fraud, and as a tool for both plaintiffs and defendants navigating an evolving litigation landscape.
AI-Fueled Fraud: A New Threat to Settlement Administration
While AI exposes companies to new litigation risks, it is also reshaping the settlement phase — creating serious vulnerabilities in claims administration.[1]
Recent class actions, including those involving consumer products, financial services and data breaches, have reported a surge in AI-generated fraudulent claims. Generative AI tools are being used to forge documents, fabricate eligibility narratives or mass-generate nearly identical submissions designed to evade traditional review of claims.
Administrators have documented instances where thousands of entries, generated from the same IP block, feature consistent language patterns or doctored receipts, all enabled by publicly available AI tools.
This influx of synthetic claims poses a multifaceted threat:
Claims administrators are responding with their own AI-based tools. Some vendors now offer machine learning platforms to detect anomalies, flag synthetic identities and validate digital artifacts against real-world data (e.g., geolocation metadata, usage logs or document version history).
However, these solutions are still evolving — and they raise new concerns about fairness, transparency and overcorrection.
There is also an emerging ethical and procedural tension: Courts expect timely and equitable distributions, but aggressive fraud-filtering algorithms may mistakenly exclude valid claims.
Plaintiffs counsel and administrators must now balance fraud prevention with due process — ensuring that AI tools used in administration are auditable, explainable and subject to human review.
The Rise of Generative Claims Fraud
Recent developments reveal a troubling evolution in the class action lifecycle: the infiltration of AI-generated fraud during claims administration.
These examples underscore a broader reality: Claims administration has become a frontline battleground in the fight against AI misuse. No longer a passive, procedural phase managed by back-end vendors, settlement administration now demands technical sophistication, robust fraud detection protocols and AI-aware oversight.
Administrators must implement digital forensics tools, pattern-recognition algorithms and validation layers to distinguish real class members from bad actors exploiting generative technologies.
This shift also implicates the judiciary, which now plays a more active role in scrutinizing the integrity of claims administration. Courts are no longer treating final approval as a rubber stamp — instead, they are increasingly demanding transparency around how administrators detect and respond to fraudulent claims.
In a number of recent cases and filings, courts and courts' guidelines have required settlement administrators to submit detailed declarations outlining their proposed data integrity and security protocols, including how they will filter invalid addresses, detect anomalies, cross-reference external databases, and block likely fraudulent claims.[6]
These judicial inquiries are not perfunctory. They reflect a growing concern that unchecked AI-driven fraud could distort class composition, inflate participation rates or misallocate settlement funds, thereby undermining the fairness and adequacy of the entire settlement process.
In some instances, courts have delayed approval or required additional rounds of auditing or claim validation before allowing distributions to proceed.[7]
As a result, claims administrators and class counsel are under increasing pressure to show that their fraud screening systems are not only in place, but also technically rigorous, unbiased and proportionate to the scale and risk of the settlement.[8] Courts want assurance that the process is defensible not only procedurally, but technologically — that it reflects a real effort to keep pace with the sophistication of AI-driven misconduct.
This emerging standard signals a broader evolution: judicial oversight of class action settlements now includes a tacit expectation of digital and algorithmic competence. Settlement administrators are no longer evaluated solely on cost-efficiency and distribution speed, but also on their ability to defend the settlement ecosystem from novel, AI-enabled threats.
Some firms are already deploying AI tools to:
While promising, these tools must be implemented with care. AI-assisted administration introduces new duties of disclosure, auditability and fairness that counsel must be prepared to defend before courts and objectors.
Ethical and Compliance Challenges in AI-Enhanced Settlement Administration
The integration of AI into litigation and settlement administration brings ethical and compliance challenges beyond those traditionally considered. In particular, claims administrators and class counsel must ensure AI does not compromise fairness, inclusivity, or procedural integrity.
Relevant ethical considerations include:
Courts are starting to take notice. In several fairness hearings, judges have questioned how fraud detection tools operate, whether legitimate claims were excluded, and whether objectors had access to meaningful review. As AI plays a growing role in this stage, courts may begin requiring disclosure of algorithmic methodologies in settlement motions and administrator declarations.
Regulatory Landscape
Looking Ahead: The Next Generation of AI-Driven Administration
As AI becomes embedded in the infrastructure of claims processing, a new regulatory and judicial oversight model is beginning to emerge. Several intersecting trends signal how courts, regulators and litigants may reshape the expectations of fairness, transparency and technical rigor in class action settlement administration.
Court-Mandated Fraud Reporting
Courts are increasingly alert to the risks posed by AI-enabled fraud schemes — including mass-produced false claims, synthetic identities and fabricated documentation. In future cases, particularly in high-dollar or data-sensitive settlements, judges may require administrators to submit formal declarations or technical reports detailing their fraud mitigation protocols.
These may include information on anomaly detection algorithms, manual review thresholds, IP and geolocation filtering, or use of third-party databases for identity verification. The aim is to establish a record that fraud prevention efforts were technically robust, unbiased and scalable, particularly when approval of final distributions is at stake.
Open-Source Audit Standards
As AI tools become more central to claim intake, eligibility determination and notice distribution, class members and objectors may challenge the fairness or accuracy of those tools. In response, there is a growing call for the development of open-source audit frameworks or standardized testing protocols to ensure these tools operate without hidden bias, disparate impact or over-exclusion of valid claims.
Future regulatory action — whether from the Federal Trade Commission, the Consumer Financial Protection Bureau, or state attorneys general — may seek to mandate algorithmic transparency in settlement administration, particularly in cases affecting protected classes or involving consumer harm.
Third-Party Monitoring of AI Denial Systems
Just as special masters or neutral experts are sometimes appointed in complex or contentious settlements, courts may begin to appoint independent monitors or technical advisers to evaluate how AI-based claim denials are handled. These monitors could review the logic behind eligibility algorithms, the rate of false positives, or the adequacy of the appeals process.
In large consumer settlements — especially those with significant denial rates — courts may require assurances that AI tools are not functioning as gatekeepers that improperly disqualify claimants without meaningful human oversight.
AI-Driven Notice Plans
Some administrators are experimenting with using AI and machine learning to optimize notice delivery, targeting potential class members through predictive models that consider geographic, behavioral and demographic data.
While this may improve reach and response rates, it also raises substantial privacy and due process concerns, especially where class members are unaware that they are being algorithmically profiled or when data sources include social media, browsing history or commercial data brokers.
Courts may soon be asked to evaluate the fairness of such plans under the "best notice practicable" requirement in Rule 23(c)(2) of the Federal Rules of Civil Procedure.
A New Litigation Frontier
As these innovations gain traction, class counsel must be prepared to confront novel legal questions:
Regulators and objectors will increasingly probe the algorithmic layer of administration, and courts will expect class counsel to respond with technical literacy, disclosure and defensible protocols. Just as claims administration once evolved from paper to digital, it is now evolving from digital to algorithmic — and the rules governing that transition are being written in real time.
Practical Takeaways for Litigators
Conclusion: Navigating a Dual-Use Technology in Every Phase
AI is redefining class action litigation on two fronts: as a source of novel legal claims and as a tool for managing complexity. But it is also fueling a quiet but consequential evolution in settlement administration, where synthetic fraud, algorithmic review and ethical tension are emerging as central concerns.
For litigators and administrators alike, adapting to this reality is essential. Those who ignore AI's effect on settlement execution may see approvals delayed, distributions challenged or fairness questioned. But those who embrace this dual-use technology — responsibly and transparently — will be better equipped to deliver equitable outcomes in the AI age.
Dominique Fite is vice president of business development at CPT Group Class Action Administrators.
The opinions expressed are those of the author(s) and do not necessarily reflect the views of their employer, its clients, or Portfolio Media Inc., or any of its or their respective affiliates. This article is for general information purposes and is not intended to be and should not be taken as legal advice.
[1] Stevenson v. Allstate Ins. Co., No. 4:15:cv-04788 (N.D. Cal. filed Mar. 1, 2024) (alleging discriminatory use of automated resume screening technology in violation of Title VII and California law); Huskey v. State Farm Fire & Casualty Co., Case No. 1:22-cv-07014 (N.D. Ill., filed Dec. 14, 2022) (challenging insurer's use of AI in claims handling, alleging disparate impact on Black policyholders); Kisting-Leung v. Cigna Corp., No. 2:23-cv-01477-DAD-CSK) (E.D. Cal. filed Apr. 21, 2023) (alleging Cigna used an algorithm to wrongfully deny medical claims without individualized review, in violation of ERISA and California law); Lopez v. Macy's, Inc., No. 2023CH05690 (Ill. Cir. Ct. Cook Cnty. filed July 14, 2023) (alleging unlawful use of facial recognition technology in violation of Illinois' Biometric Information Privacy Act); Gross v. Madison Square Garden Ent. Corp., No. 1:23-cv-03380 (S.D.N.Y. filed Nov. 6, 2023) (challenging MSG's use of facial recognition under New York City's biometric ordinance to deny entry to attorneys, alleging First Amendment and civil rights violations); EEOC v. iTutorGroup, Inc., No. 1:22-cv-02565-PKC-PK (E.D.N.Y. filed May 5, 2022) (alleging company used AI hiring software to reject older female applicants in violation of the Age Discrimination in Employment Act and Title VII); Mobley v. Workday, Inc., No. 3:23-cv-00770-RFL (N.D. Cal. filed Feb. 21, 2023) (alleging discriminatory bias in Workday's AI-based applicant screening tools in violation of Title VII, Section 1981, and California law); In re Meta Soc. Media Adolescent Addiction Litig., No. 4:22-cv-03047, MDL No. 3047 (N.D. Cal. filed Oct. 6, 2022) (consolidated multidistrict litigation alleging that Meta's algorithmic features caused addiction and mental health harms among minors); Doe v. YouTube LLC, No. 4:20-cv-07493 (N.D. Cal. filed June 30, 2022) (alleging YouTube's algorithm facilitated and promoted sexual exploitation of minors).
[2] In re Juul Labs, Inc. Mktg., Sales Pracs. & Prods. Liab. Litig., No. 3:19-md-02913 (N.D. Cal. centralized Oct. 2, 2019) (reports of mass, AI-tainted claim submissions tied to the JUUL/Altria consumer settlement surfaced in late 2023 and were discussed in court and the trade press through spring 2024).
[3] See, e.g., In re HCA Healthcare, Inc. Data Security Litig., No. 3:23-cv-00684 (M.D. Tenn. filed July 12, 2023) (arising after HCA disclosed on July 10, 2023, that an unauthorized party posted patient contact data affecting approximately 11 million patients to an online forum); In re LoanCare Data Security Breach Litig., No. 3:23-cv-01508 (M.D. Fla. filed Dec. 27, 2023) (stemming from a Nov. 19, 2023, cybersecurity incident involving LoanCare and Fidelity National Financial, later consolidated for settlement proceedings); In re Phila. Inquirer Data Security Litig., No. 2:22-cv-04185 (E.D. Pa. filed Oct. 19, 2022) (captioned Braun v. The Philadelphia Inquirer, LLC, involving a May 2023 ransomware attack attributed to the "Cuba" group); Nelson v. Connexin Software, Inc., No. 2:22-cv-04676 (E.D. Pa. filed Nov. 23, 2022) (arising from an Aug. 2022 breach compromising pediatric patient records, with discovery dated Aug. 26, 2022).
[4] Darren K. Cottriel & Alexander W. Prunka, Rising Fraudulent Claims Submitted to Class Action Settlement Funds Heighten Settlement Risk, Jones Day (June 21, 2024).
[5] Mike Scarcella, US court finds 'robust' anti-fraud safeguards in Novartis settlement, Reuters (July 26, 2024). [
6] See In re Novartis & Par Antitrust Litig., No. 1:18-cv-04361-AKH-SDA (S.D.N.Y. May 31, 2024) (order noting the administrator's use of AI-based fraud detection, IP monitoring, and behavioral analysis); Procedural Guidance for Class Action Settlements, U.S. Dist. Ct. N.D. Cal. (rev. Dec. 5, 2018), https://cand.uscourts.gov/forms/procedural-guidance-for-class-action-settlements/.
[7] See Jimenez v. Artsana USA, Inc., No. 7:21-cv-07933-VB (S.D.N.Y. Oct. 18, 2023) (order withholding approval pending resolution of suspected fraudulent claims); In re Payment Card Interchange Fee & Merch. Disc. Antitrust Litig., 330 F.R.D. 11, 29–30 (E.D.N.Y. 2019) (addressing fraudulent claims and a fake settlement website).
[8] See N.D. Cal. Procedural Guidance, supra (requiring disclosure of administrator's protocols and a post-distribution accounting); In re Novartis & Par, No. 1:18-cv-04361-AKH-SDA.
