April 7, 2026

Enforcers’ Intensifying Focus on Algorithmic Pricing Tools

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Key Takeaways

• Scrutiny of algorithmic pricing models – particularly in sectors that rely heavily on data – is accelerating across federal and state enforcers, with California now banning the use of “common pricing algorithms.”
• While debate continues over how to evaluate pricing algorithms, courts are increasingly probing plus‑factor evidence, such as adherence to algorithmic outputs, market dynamics and the competitive context in which the technology operates.
• Companies using algorithmic or analytic pricing software – especially in healthcare, consumer products and other complex sectors – should anticipate more intensive scrutiny of vendor relationships and rising expectations around technical transparency. At this year’s ABA Antitrust Spring Meeting, a panel of practitioners and economists discussed the rapid expansion – and rising scrutiny – of algorithmic pricing tools in the healthcare industry. Although the panelists, including an Assistant Attorney General from the Office of the Attorney General for the District of Columbia, approached the issue from different angles, they agreed that scrutiny of algorithmic pricing tools in data-driven sectors is intensifying, with a particular focus on how technology design, data inputs and vendor relationships may influence competitive outcomes.

The panel’s discussion underscored that companies using these tools – within healthcare, consumer products and other complex, data-driven industries – should be prepared for increased attention in a rapidly evolving enforcement landscape.

Enforcement Update

The panel also highlighted how the Department of Justice Antitrust Division has continued to file Statements of Interest in algorithmic pricing cases, repeatedly emphasizing the “starting point” theory advanced by the DOJ and the Federal Trade Commission. [1] The panel noted that, in the agencies’ view, competitors may be considered to coordinate simply by agreeing to use the same algorithm to generate benchmark prices – even if their final prices differ. The DOJ also cautions that providing sensitive data to an algorithmic intermediary can carry the same risks as sharing it directly with competitors.

These themes track broader policy developments, including recent state efforts to regulate the use of shared pricing algorithms. The panel’s discussion of recent state policy developments focused on California’s Assembly Bill 325 (AB 325), which took effect Jan. 1, 2026. AB 325 prohibits the use of “common pricing algorithms” that rely on competitor data to stabilize prices or wages and introduces potential criminal liability for coercive conduct. The statute defines the term broadly to encompass “any methodology, including a computer, software, or other technology, used by two or more persons, that uses competitor data to recommend, align, stabilize, set, or otherwise influence a price or commercial term.” [2] Panelists noted that California’s early move to regulate pricing systems may signal growing state‑level interest in directly addressing algorithmic pricing, particularly in sectors such as healthcare, where cost and affordability are central public concerns.

Per Se vs. Rule‑of‑Reason Approaches

The panel also examined how courts should analyze algorithmic pricing under existing legal frameworks. Certain panelists cautioned against expanding per se treatment to algorithmic pricing tools in the absence of an actual agreement among horizontal competitors, particularly in instances where the alleged conduct lacks traditional indicators of collusion, such as direct communications or coordinated decision‑making. They argued that relying on the same vendor or pricing software does not, on its own, establish unlawful concerted action, and they emphasized that parallel pricing outcomes are insufficient without meaningful “plus factors” demonstrating coordination.

Other panelists, however, pointed to plus factors seen in recent healthcare cases – such as prior histories of firms colluding, uniform reliance on the same vendor inputs, and system features that make deviation difficult – that they believe can transform lawful parallel conduct into unlawful coordinated action facilitated by shared technology. Economists added that while algorithms may speed up pricing alignments, they do not inherently signal unlawful collusion. They highlighted that antitrust analysis depends heavily on factors such as timing, strategic incentives, a firm’s ability to deviate from benchmarks and whether technology simply increases pricing responsiveness rather than facilitating agreement.

The panel ultimately agreed, however, that these issues will likely continue to surface as discovery in active cases reveals more about the design and implementation of algorithmic pricing models in the healthcare industry. As only one major matter has progressed into discovery to date, agencies and litigants are anticipating that source code, training data and metadata on user behavior may reveal more about how these tools influence pricing outcomes.

Courts Take a Closer Look at Pricing Algorithms

Panelists observed that recent healthcare‑sector litigation – most notably In re MultiPlan Health Insurance Provider Litigation – demonstrates a growing judicial willingness to view reliance on a shared vendor and pricing‑recommendation technology as a potentially actionable horizontal agreement. [3]

In MultiPlan, the plaintiffs’ claims survived a motion to dismiss based on allegations that multiple third-party payors responsible for negotiating reimbursement rates for healthcare services relied heavily on a shared vendor’s pricing recommendations, adhered closely to those outputs and supplied competitively sensitive information to a common intermediary. The court’s analysis in MultiPlan underscores that no single allegation is dispositive. Rather, courts are examining overall adherence patterns, surrounding market behavior and the competitive context in which pricing technology is deployed.

Although panel members cautioned that the MultiPlan plaintiffs’ theories stretch traditional hub‑and‑spoke concepts beyond their intended bounds, arguing that software vendors are not market participants and should not be viewed as serving the “hub” of an alleged conspiracy merely because multiple competitors use the same tool. Meanwhile, economists added that the MultiPlan decision – similar to other cases involving the use of algorithmic pricing models in the healthcare industry – rests on broad, prediscovery allegations. Despite this, panelists agreed that the trend is unmistakable: Courts are increasingly reluctant to dismiss vendor‑facilitated coordination theories at the pleading stage, suggesting that algorithmic interactions are likely to face continued judicial scrutiny.

Economic Evidence and the Challenges of Assessing Collusion in Data-Driven Markets

Economists on the panel discussed the complexities of applying collusion frameworks in complex industries, such as the healthcare market, that are heavily regulated, offer highly bespoke products and operate in a manner that is often opaque to consumers. They outlined key indicators to look for when assessing whether algorithmic tools facilitate coordination: convergence of adoption timing, conduct inconsistent with unilateral incentives, evidence of punishment for deviation, shifts in price dispersion and stability of alleged agreements. Panelists also emphasized that pricing changes may coincide with broader market developments – such as regulatory revisions or structural shifts common in the healthcare industry – making it challenging to distinguish algorithm‑driven effects from other market dynamics as these cases proceed.

What Companies Should Do Now: Practical Steps To Mitigate Risk

The panel closed with a practical discussion on how companies can mitigate risk when using algorithmic pricing tools. A key theme was the importance of understanding how and where data moves through pricing-related systems, especially when competitively sensitive information may be shared, commingled or repurposed by vendors in ways that could create legal exposure. Companies relying on these tools should start by mapping their data flows to identify where risks may arise.

Panelists also underscored the importance of reviewing contracts with algorithmic pricing vendors. To that end, companies should consider incorporating explicit contractual terms that prohibit data commingling, require vendors to delete company data upon request, and include safeguards tailored to vertically integrated healthcare entities. The panel further urged companies to strengthen their compliance programs, particularly by training and educating technical and IT teams who work directly with these systems and are best positioned to identify risks in how algorithmic pricing tools are designed and used.

Companies should also prepare for deeper technical investigations, as enforcement agencies increasingly expect firms to understand and articulate how their algorithmic models work. Enforcers will want to understand how models are designed, trained and used day‑to‑day, including override processes, adherence patterns and internal guidance. Being able to clearly explain how the system works – and how employees interact with it – will be essential.

Takeaways

• Expect continued scrutiny of algorithmic pricing tools. Courts and enforcers are increasingly willing to probe how shared algorithms influence pricing behavior and are less inclined to dismiss coordination theories early.
• Clear data transparency around pricing tools is now essential. Companies should be prepared to clearly explain how data is used, shared and integrated within pricing systems, including vendor access and model design.
• Vendor relationships require careful management. As reliance on third‑party pricing tools expands, so does the importance of strong contractual safeguards and ongoing oversight.
• The enforcement landscape is shifting quickly. Companies should actively track new legislative developments, government enforcement actions and private litigation trends to ensure compliance programs evolve with the landscape. We expect antitrust scrutiny of algorithmic pricing to continue intensifying, making it essential for companies to treat these tools as an active and ongoing compliance priority.

[1] Statement of Interest of the United States, In re MultiPlan Health Insurance Provider Litigation, No. 1:24-cv-06795, MDL No. 3121 (N.D. Ill. Mar. 27, 2025), Dkt. No. 382, available at https://www.justice.gov/atr/media/1394631/dl?inline; Statement of Interest of the United States, Cornish-Adebiyi v. Caesars Entertainment, Inc., No. 1:23-cv-02536 (D.N.J. Mar. 28, 2024), Dkt. No. 96, available at https://www.justice.gov/archives/opa/media/1345721/dl?inline.

[2] Cal. Assemb. B. 325, 2025-26 Leg., Reg. Sess., ch. 338, § 1 (Cal. 2025) (codified at Cal. Bus. & Prof. Code § 16729).

[3] In re MultiPlan Health Insurance Provider Litigation, No. 24 C 6795 (N.D. Ill. Jun. 3, 2025), Dkt. No. 428.

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DISCLAIMER: Because of the generality of this update, the information provided herein may not be applicable in all situations and should not be acted upon without specific legal advice based on particular situations.
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