Amanda Chaboryk and a team from Epiq highlight the opportunities for AI use in collective actions
The UK’s collective actions regime is entering a period of both rapid expansion and transformation. According to the Competition Appeal Tribunal and Competition Service Annual Report [1], the Tribunal issued 76 judgments and made 475 orders in the year ending March 2024, with 268 cases carried forward into the next year, reflecting a significant increase in both volume and complexity. This surge in activity underscores the growing societal and economic importance of collective proceedings, particularly in consumer and competition law and reflects a wider global pattern. In the United States, securities class actions continued to accelerate in 2024, with 222 federal filings and more than $3.7 billion recovered in settlements. Cybersecurity and data‑breach cases were among the fastest‑growing categories, with three of the year’s ten largest settlements totalling $560 million. Alongside escalating antitrust and ESG‑related claims, both jurisdictions are now managing cases that are larger, more data‑heavy, and procedurally more complex than ever, creating significant operational pressures for those responsible for administering collective redress at scale.[2]
Yet, despite this momentum, the use of artificial intelligence in collective actions remains largely unexplored. While AI is being embedded across other areas of the justice system, from transcription and legal triage to judicial analytics its application in group litigation has lagged. This presents a compelling opportunity: AI can dramatically improve how collective actions are administered, from automating claims intake and personalising claimant communications to analysing vast datasets and enhancing transparency for funders and regulators. As the regime matures, embracing AI is not just a matter of efficiency, it’s a strategic imperative to ensure that collective redress remains scalable, accessible, and equitable.
AI and Access to Justice
The relationship between AI and access to justice has been increasingly discussed, particularly in the promise of technology facilitating greater legal empowerment, reducing barriers to legal services, and enhancing the efficiency and fairness of judicial systems. This intersection of AI and justice is now being institutionalized through major initiatives like the Oxford Institute of Technology and Justice, a unique collaboration between the University of Oxford’s Blavatnik School of Government and the Clooney Foundation for Justice (CFJ). The Oxford Institute for Technology and Justice, launched with the Clooney Foundation for Justice, uses AI to expand global access to justice, including tools for vulnerable communities and standards for AI in courts. Meanwhile, the UK Ministry of Justice’s AI Action Plan aims to embed AI across the justice system – courts, prisons, and probation – through tools like transcription, legal triage, and a dedicated Justice AI Unit.
Access to Justice and Collective Actions
One area that has seemingly not received the attention it deserves is the potential of AI in collective actions. Despite the government’s broad ambitions to embed AI across the justice system, as outlined in the MOJ’s AI Action Plan and supported by the Justice AI unit, there is at present no mention of AI being applied to collective redress or group litigation in any of the official strategy documents or consultations reviewed. While the UK Ministry of Justice’s AI initiatives focus on public sector justice delivery, like courts, prisons, and legal aid, the collective actions regime operates largely in the private legal sphere. These cases are typically brought by claimant law firms, backed by litigation funders, and overseen by the Competition Appeal Tribunal (CAT) rather than the civil courts. This distinction matters because collective actions are driven by private actors, claimant law firms, litigation funders and claims administrators, who operate outside the remit of public justice reform. While collective actions are not part of the public justice system, they serve a public interest function by enabling consumer redress at scale. This makes them a crucial tool for access to justice, especially in cases where individual claims would be too small or complex to pursue alone. In this way, collective actions, though privately initiated, perform a vital public role: they hold powerful entities accountable, deter unlawful conduct, and ensure that consumers can enforce their rights in practice, not just in principle. Their impact extends beyond individual compensation, contributing to market fairness and systemic accountability.
Collective Actions in the Public Interest: Two Illustrative Cases
Two UK collective actions with CAT-approved settlements highlight how private litigation can deliver public justice outcomes. For example, in Merricks v Mastercard, the CAT approved a £200 million settlement, with £100 million allocated for distribution to up to 46 million UK consumers who allegedly paid inflated prices due to unlawful interchange fees. This case set a precedent for large-scale consumer redress and market accountability. In Mark McLaren Class Representative Ltd v MOL (Europe Africa) Ltd and Others, the CAT approved a collective settlement with CSAV, one of several defendants in a cartel case involving inflated vehicle shipping costs. These costs were allegedly passed on to UK consumers and small businesses purchasing or financing vehicles, demonstrating how collective actions can address supply chain harms that affect end users CAT.
It is most certainly an exciting time for the UK’s collective actions landscape. For the first time, a female, Dr Rachel Kent, has successfully acted as a class representative in a certified collective action, marking a significant milestone in the development of the regime. Her case concerns alleged anti-competitive conduct in the digital marketplace and seeks compensation on behalf of millions of UK consumers, reflecting the growing maturity and societal relevance of collective proceedings. Against this backdrop, the UK government launched a consultation on the opt-out collective actions regime in August 2025, seeking views on its effectiveness, funding, and access to redress. Yet, notably, the consultation made no reference to the role of technology or AI in improving the efficiency, accessibility, or administration of these proceedings, an area of growing relevance that remains underexplored despite its potential to transform how large-scale claims are managed and delivered.
This presents a significant opportunity for the industry, particularly law firms, funders, technology companies and claims administrators, to further embrace AI within the collective actions space. As the volume and complexity of these cases grow, so too does the need for scalable, intelligent solutions. When adopted responsibly, AI can enhance every stage of the process.
There is a clear parallel with industries that already operate mature, production-grade AI infrastructure particularly financial services, aviation operations, and parts of healthcare, where high volumes, strict regulation, and real-world consequences have forced organisations to treat AI as an operational discipline rather than a pilot project. In these sectors, the hard-won lessons are consistent: strong data foundations, clear ownership and change control, monitoring for drift and failures, and governance that produces an audit trail of what changed, when, and why. Those same patterns translate well to collective actions, where administrators must be able to scale decisions safely, explain outcomes to stakeholders, and maintain public trust.
Generative AI in particular offers transformative potential due to its advanced natural language processing (NLP) capabilities. Law is fundamentally a language-based discipline as it is built on statutes, pleadings, contracts, judgments and evidence, all of which rely on precise and often complex language. Generative AI excels at understanding, generating, and summarising legal and factual content, making it especially valuable in collective actions where vast volumes of unstructured data, such as claims, correspondence, and supporting documents, must be processed efficiently. It can support the drafting of notices, automate claims intake, translate and personalise communications, and identify patterns in claimant data. Additionally, AI-powered legal redlining tools, which have shown marked improvements in accuracy through generative models, can assist in reviewing and finalising legal documents, ensuring consistency, reducing drafting time, and minimising human error.
AI also plays a critical role in claimant engagement. Chatbots powered by generative AI can handle a wide range of claimant queries, reducing the burden on call centres and providing immediate, accurate responses. These bots can triage complex issues to human agents when needed, ensuring that claimants receive timely and appropriate support. Importantly, as chatbots interact with a broader and more diverse set of queries, they continuously improve, learning from linguistic variation, sentiment, and context, to deliver more nuanced and effective responses over time. However, these systems must be trained and tested on representative datasets to guard against algorithmic bias that could systematically disadvantage particular demographics, an outcome that would directly undermine the access-to-justice objectives collective actions are designed to serve. This not only enhances claimant experience but also contributes to operational efficiency and scalability.
Generative AI is also highly effective at harvesting and analysing data across large volumes of cases. This enables administrators and funders to better capture learnings; for example the associated costs at each stage of the lifecycle; claimant engagement metrics; and examples of cost-to-damages ratios from lived experience. These insights can be used to refine budgeting, improve forecasting, and support funding decisions with real-world benchmarks, ultimately helping funders assess risk and return with greater precision.
The more efficiently these processes are handled, the more cost savings can be achieved—reducing legal and administrative fees and allowing a greater proportion of settlement funds to be directed to claimants. In this way, generative AI not only improves operational performance but also strengthens the core purpose of collective actions: delivering meaningful, accessible, and equitable redress to large groups of affected individuals.
Agentic AI‘s are advanced artificial intelligence systems that operate as autonomous agents, taking initiative to make decisions and complete tasks with limited human oversight. Unlike traditional software, these systems can independently set goals, adapt to changing situations, and improve over time through learning.
The word “agentic” refers to the ability to act with agency or independence. In practice, agentic AI can handle processes such as gathering evidence and verifying claimant information, working much like a virtual smart assistant. In the context of UK collective actions and legal claims, agentic technologies can use secure digital interfaces, such as Restful APIs,to automatically validate details ( proof of property ownership or employment status perhaps) by connecting directly to authoritative public databases such as those managed by HMRC or the Land Registry.
The UK government’s “Make Tax Digital” initiative is a flagship programme designed to modernise the tax system by requiring individuals and businesses to keep digital records and submit tax information through secure online platforms. Launched by HM Revenue & Customs (HMRC), its core aim is to make tax administration more effective, efficient, and easier for taxpayers by reducing paperwork, minimising errors, and enabling real-time reporting. Similarly, HM Land Registry has undertaken significant digital transformation by moving property registration and title records online, providing secure digital access to property data and ownership information.
These digital reforms create an ecosystem where authoritative data is not only digitised but also accessible via secure APIs and online services. Agentic AI technologies can leverage these digital infrastructures to streamline and automate key processes for class action claimants or other group litigants. For example, in collective actions requiring proof of property ownership, employment, or tax status, agentic AI systems can securely interface with HMRC or Land Registry databases to validate claimant information in real time. This reduces the reliance on physical documents, which may be lost or difficult to obtain, and lowers barriers for individuals to participate in collective redress.
By harnessing the capabilities of these government digital services, agentic AI can efficiently gather, verify, and process large volumes of claimant data, ensuring that eligibility checks are accurate and inclusive. This not only accelerates the administration of group claims but also enhances two pillars of justice: fairness and accessibility.. Ultimately, integrating agentic AI with initiatives like “Make Tax Digital” and Land Registry’s digital services empowers more individuals to access justice, especially in complex or large-scale legal proceedings where timely and accurate information is essential. This allows claimants who might have lost or never received physical paperwork to still participate in collective redress, making the legal process more inclusive and efficient.
Agentic technologies are a step beyond traditional AI, as they can manage entire workflows and make real-time decisions, rather than just automating single tasks. Their adoption in legal processes helps lower barriers to participation, streamlines claim validation, and supports robust privacy and data protection standards.
Conclusion
Whether a case is opt-in or opt-out, AI has the potential to support critical workflows across the collective actions’ lifecycle. These proceedings often involve regulatory scrutiny and high public visibility, making accuracy, transparency, and responsiveness essential. As AI tools become more deeply embedded in the administration of collective actions, the opportunities they present through greater efficiency, scalability and claimant engagement, must be matched by robust safeguards. This includes ensuring PII protection, privacy by design algorithmic fairness, and auditability.
With great opportunity comes great responsibility, a principle that arguably applies to all areas of life, not just law. Organisations deploying AI in this space must be prepared not only to demonstrate how these tools improve outcomes, but also how they safeguard the personal data of millions of claimants, maintain clear audit trails, uphold compliance, accountability, and public trust.
[1] And Accounts 2023-2024
[2] bbd-gca-annualreport25-1.29.25-final.pdf
Amanda Chaboryk, Client Services Director, Epiq Class Actions & Restructuring
Clinton Smith is a senior technology and product leader at Epiq, specialising in the design and delivery of secure, scalable digital platforms for collective actions, claims administration and complex legal workflows.
Mathew Hookings is a Lead Developer at Epiq and heads API development, including payment integrations and Power Platform development for complex remediation programmes.
