DOJ Warns of Potential Antitrust Risks With Pricing Algorithms

The US Department of Justice recently reaffirmed its view that using third-party pricing algorithms can raise significant antitrust risks. Businesses need to understand this development to proactively assess and modify their pricing practices before facing costly antitrust investigations that could disrupt operations and damage their reputation.

In a statement of interest that partially backed plaintiffs in multidistrict litigation against MultiPlan Inc., the DOJ asserted that using a common pricing algorithm can qualify as concerted action under Section 1 of the Sherman Act, 15 U.S.C. § 1, even if the competitors do not always use the algorithm in the same way.

Competitors’ exchange of competitively sensitive information can violate Section 1 even if ongoing litigation demonstrates the continued state and federal legal and regulatory scrutiny facing algorithmic pricing practices across industries.

In the MultiPlan case, hospitals allege that the MultiPlan data analytics firm operates as the hub of an illegal conspiracy, coordinating with major insurers such as Aetna and Cigna to suppress out-of-network reimbursement rates through algorithmic pricing tools.

MultiPlan has defended its pricing tool as merely providing recommendations based on publicly available data, arguing that it doesn’t set requirements or payment limits and that healthcare providers retain full discretion over reimbursement decisions. On June 3, 2025, Judge Matthew F. Kennelly denied MultiPlan’s motion to dismiss, finding that plaintiffs sufficiently alleged parallel conduct among insurers using MultiPlan’s system in violation of antitrust laws.

The DOJ’s interest in MultiPlan aligns with other high-profile cases, such as its lawsuit against RealPage Inc., whose AI-driven pricing tools for rental housing allegedly facilitated collusion among landlords. Similar legal actions have been brought against pricing tools in the hotel and apartment rental industries, signaling a shift in antitrust focus toward algorithmic price-setting across industries.

To help ensure compliance with the antitrust laws, and limit unwanted government scrutiny and costly private litigation, businesses should take a proactive approach in reviewing, monitoring, and documenting their use of pricing algorithms.

Review and Audit Algorithmic Pricing Models. Companies should evaluate their pricing algorithms at the design stage and on an ongoing basis to identify any features that could lead to price coordination. The goal is to ensure that pricing decisions are based on independent business strategies rather than market-wide patterns that regulators might view as collusive and always make independent determinations of a fair price.

• Document that the use of AI and algorithmic pricing are only one factor in determining a fair price.
• Clearly document the other factors considered. Regularly audit pricing algorithms for potential coordination risks. One way to do this is to benchmark pricing against the prices recommended by the third-party tools.
• Examine data inputs to ensure they don’t rely on sensitive competitor information.
• Assess industry-standard tools for risks of inadvertently facilitating parallel pricing.
• Document independent business justifications for algorithmic pricing choices.

Implement Strong Monitoring and Controls. Ongoing oversight is critical for catching potential issues before they become legal liabilities. Companies should have automated tracking systems and human oversight in place to ensure their algorithms function as intended.

• Set up real-time monitoring for algorithm-driven price adjustments.
• Assign clear oversight responsibility for pricing algorithm management.
• Maintain audit trails for pricing decisions to ensure transparency.
• Develop clear response protocols for addressing any pricing anomalies.
• Set and monitor the other factors that determine pricing, and update the guidelines.

Train Staff on Antitrust Compliance. Employees responsible for pricing decisions should be well-versed in the technical aspects of algorithmic pricing and the legal risks associated with it. Regular training can help teams recognize red flags and avoid antitrust pitfalls.

• Conduct periodic training sessions, tailored for pricing and strategy teams.
• Establish clear accountability structures for those overseeing pricing algorithms.
• Perform routine compliance audits to ensure adherence to best practices.
• Develop action plans for addressing potential legal concerns.
  

Maintain Thorough Documentation. If regulators come knocking, having clear, well-organized documentation can help demonstrate that pricing algorithms are designed and used in a lawful manner. Businesses should keep records that explain how pricing models function and why they make specific pricing decisions.

• Maintain logs of algorithm design decisions and updates.
• Document the business rationale behind pricing strategies.
• Track any modifications to pricing algorithms over time.
• Ensure there is a clear, auditable trail for all major pricing decisions.

Avoid Breeding Customer Distrust. In addition to monitoring the regulatory landscape, businesses should carefully consider transparency issues surrounding the use of algorithmic pricing tools. Disclosing the use of AI-driven pricing systems upfront may risk alienating potential customers who perceive such technology as manipulative or unfair. Conversely, a non-disclosure creates significant reputational vulnerabilities if these practices later become public, as witnessed with RealPage and MultiPlan.

High-profile cases demonstrate the potential for severe brand damage. Uber suffered significant reputational harm when its surge pricing algorithm dramatically increased fares in central London following a terrorist attack. Businesses should anticipate that customers may perceive algorithmic pricing as inherently leading to higher costs, requiring proactive management of these perceptions.

By taking these steps, businesses can help reduce their exposure to legal risks while still leveraging the benefits of algorithmic pricing. The key is to ensure pricing always remains independent, transparent, and justifiable based on legitimate business needs rather than market coordination.

Businesses using AI-driven pricing must be vigilant about potential antitrust risks. If you rely on algorithmic pricing, now is the time to review your practices to ensure compliance with competition laws.

This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law, Bloomberg Tax, and Bloomberg Government, or its owners.

Author Information

Karen Rigberg is of counsel at Nelson Mullins and spent 20 years in-house for a major automaker and continues to help companies of all sizes implement tailored antitrust compliance programs.

Bart Daniel is partner at Nelson Mullins and a former federal prosecutor who tried civil and criminal antitrust cases and now defends them in his private practice.

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