USA – CALIFORNIA Trends and Developments Contributed by: Dan Mogin and Eric Miller, Mogin Law LLP
As consolidation accelerates, expect antitrust enforce- ment to become a key issue in the AI era. Dive Deeper: To track major acquisitions and invest- ments, visit the Mogin Law AI Deal Table, an inter- active AI acquisition tracker. Also read Artificial Intel- ligence Roundup on the Mogin Law Blog, a resource for the latest developments in antitrust, including AI matters. DOJ Antitrust Whistleblower Rewards Program Announced in July 2025, the DOJ’s Antitrust Whistle- blower Rewards Program offers monetary rewards of 15–30% of criminal fines recovered for whistle-blow- ers who report price fixing, bid rigging, and market allocation. The program is administered in partnership with the US Postal Inspection Service and the USPS Office of Inspector General. It complements the DOJ’s Procure- ment Collusion Strike Force and aims to break the secrecy in procurement fraud. Recent cases like the Omnicell settlement (USD4.4 million) and the Korean fuel supplier bid-rigging case (USD300 million) illustrate the power of whistle-blow- er-initiated antitrust enforcement. Dive Deeper: Author Dan Mogin, Managing Partner, Mogin Law LLP and Julie Keeton Bracker, Partner, Bracker & Marcus LLC partnered on a webinar to educate attorneys and stakeholders on how to navi- gate the whistle-blower process, assess eligibility, and understand confidentiality protections. Readers who would like a deeper dive on this subject should watch the Mogin Law webinar on the DOJ’s Antitrust Whistleblower Program 2025. The Essential Role of Data in Administering Antitrust Class Action Settlements Antitrust class actions involve complex issues of price-fixing, monopolistic practices, and market manipulation, requiring sophisticated tools to identify injured class members and allocate damages. Claims administrators play a pivotal role in ensuring the integ- rity of these processes, leveraging defendants’ data, third-party records, and class member documentation
to verify claims and prevent uninjured individuals from recovering class funds. Key arguments in antitrust class actions • Use of Defendants’ Data: In price-fixing cases, defendants’ sales records are often central to verifying claims. Claims administrators cross-check these records to confirm that claimants made qualifying purchases. For example, in In re Ethylene Propylene Diene Monomer (EPDM) Antitrust Litiga- tion, defendants’ sales and invoice records were used to identify class members, ensuring that only those directly affected by the alleged price-fixing could recover damages. • Third-Party Data: When intermediaries sepa- rate claimants from defendants, third-party data becomes essential. In pharmaceutical antitrust cases, data from pharmacy benefit managers (PBMs) is used to distinguish between direct and indirect purchasers. For instance, PBM data was critical in In re Ranbaxy Generic Drug Application Antitrust Litigation, where it helped identify eligible class members and weed out uninjured parties. • Class Member Proof: In cases where defendants’ or third-party data is insufficient, class members may submit transaction records, receipts, or sworn affidavits to prove their injuries. These submissions are subject to rigorous audits to ensure accuracy and prevent fraudulent claims. Statistics and trends California has been a hotspot for antitrust litigation, with claims administrators increasingly relied upon to manage large, complex class actions. While specific statistics are not provided in the document, the grow- ing reliance on claims administrators reflects broader trends, as outlined below. • Efficiency gains: Claims administrators streamline the process of identifying injured class members, reducing the burden on courts and expediting set- tlements. • Data-driven processes: The use of defendants’ and third-party data has become a standard practice, ensuring that claims are verified with precision. • Fraud prevention: Audit practices are essential in both front-end and back-end processes, using AI-driven systems to minimise and combat fraudu-
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