Navigating Antitrust Risks in Algorithmic Pricing

May 19, 2025 –  As algorithmic pricing becomes increasingly prevalent across industries, companies must be vigilant about the antitrust risks associated with these technologies. Recent enforcement actions and legal developments underscore the importance of ensuring that the use of pricing algorithms complies with antitrust laws.

Key Antitrust Risks

1. Collusion via Shared Algorithms: The use of common pricing algorithms by competitors can prompt allegations of collusion, either explicitly or tacitly. For instance, the Department of Justice has filed a civil antitrust lawsuit against RealPage alleging that its software enabled landlords to coordinate and raise apartment rental prices by sharing nonpublic, competitively sensitive information on rental rates, effectively eliminating price competition and discounts. U.S. et al. v. RealPage, Inc. et al., 1:24-cv-00710 (M.D.N.C. Aug. 23, 2024). In January, the DOJ expanded the case against the software vendor substantially by adding six large landlords as defendants to the matter. Private class actions have also been brought recently against hotel casinos,¹ heavy equipment rental companies,² manufactured housing companies³ and health care insurers.⁴
2. Information Sharing Concerns: Algorithms that aggregate and analyze nonpublic, competitively sensitive information from multiple competitors can raise red flags. The Federal Trade Commission (FTC) and DOJ have emphasized that price-fixing through algorithms is illegal and that agreements to use shared pricing recommendations or algorithms can be unlawful even if co-conspirators retain some pricing discretion.⁵
3. Criminal Liability and Enforcement Trends: While previously rare, criminal antitrust charges related to algorithmic pricing are becoming more common. The DOJ’s investigation into RealPage included executing a search warrant at a corporate landlord’s office, signaling a willingness to pursue criminal enforcement in cases involving pricing algorithms. ⁶

Risk Mitigation Strategies

To reduce antitrust risks associated with algorithmic pricing, companies should consider the following steps:

• Independent Pricing Decisions: Ensure that pricing decisions are made unilaterally and not influenced by shared algorithms or nonpublic competitor data. Companies should avoid agreeing with competitors to use pricing algorithms, in order to avoid the appearance of an intent to fix prices.
• Understand Algorithm Functionality: Gain a thorough understanding of how pricing algorithms operate, including data sources and decision-making processes. Limit the algorithm’s inputs to public data and the company’s internal data to reduce antitrust risk.
• Vendor Due Diligence: When using third-party vendors for pricing algorithms, conduct comprehensive due diligence to assess their data sources, their compliance measures and whether they serve competitors. Be cautious with third-party software that uses competitors’ confidential data to inform price recommendations.
• Compliance Policies and Training: Update antitrust compliance policies to address risks associated with algorithmic pricing. Provide training to employees, particularly those in pricing and IT development roles, to recognize and mitigate antitrust risks.
• Monitor and Audit Algorithms: Regularly assess the performance and impact of pricing algorithms to ensure they do not facilitate an anticompetitive exchange of information among competitors. Implement oversight mechanisms, especially for self-learning algorithms, to prevent unintended collusion.

Conclusion

The adoption of algorithmic pricing tools can offer significant efficiencies and competitive advantages, but it can also raise significant antitrust risks. Companies must proactively implement compliance measures, conduct thorough assessments of their pricing strategies and stay informed about legal developments to navigate this complex landscape. 

For tailored advice and assistance in evaluating your company’s use of pricing algorithms, please contact our Antitrust & Competition practice group.