Algorithmic Pricing: Comparing US and Korean Antitrust Regulation

In this contribution to Steptoe’s StepAhead Antitrust & Competition blog, authors Lee Berger, John J. Kavanagh, Weisiyu Jiang & David Brunfeld (Steptoe) and Junyeun Cho & Dohyung Kwon (Bae, Kim & Lee LLC) explain how antitrust risks are evolving as competitors increasingly share information through intermediaries, benchmarking platforms and algorithmic pricing tools. They compare US and Korean approaches, noting that technology does not shield companies from traditional antitrust principles.

The authors examine how direct exchanges of competitively sensitive information have long attracted antitrust scrutiny. They highlight Korea’s 2020 reforms and the KFTC’s first enforcement action under the new rules, which involved major banks exchanging granular loan data. The case underscores regulators’ concern that information sharing can reduce uncertainty between competitors and weaken competitive pressure.

The analysis then turns to third-party intermediaries and AI-driven pricing. The US Agri Stats case shows how benchmarking services can facilitate coordination by distributing detailed competitor data, while the RealPage litigation illustrates the emerging risks of algorithms that recommend prices using non-public competitor information. The authors note that important questions remain unresolved, particularly over when algorithmic pricing crosses the line into unlawful coordination.

The authors conclude that the key issue is not how information is transmitted, but whether it enables competitors to coordinate rather than compete independently. Companies should therefore scrutinize the source, sensitivity, frequency and granularity of data used in information-sharing arrangements and pricing algorithms, particularly where non-public competitor information is involved…

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