Competition Law and Algorithmic Compliance Monitoring Systems

1. Introduction

Algorithmic compliance monitoring systems are software-based tools used by businesses to monitor commercial conduct and identify possible breaches of internal policies or legal requirements. In competition law, these systems can examine matters such as pricing behaviour, communications, discounts, distribution practices, competitor interactions, market data, bidding patterns, and transactions.

The basic idea is useful: instead of relying entirely on occasional manual audits, a company can continuously analyse large quantities of data and flag unusual behaviour for review.

However, the same technology can create competition-law problems. A system designed to monitor compliance may become problematic if it facilitates the exchange of competitively sensitive information, monitors whether competitors are following coordinated prices, automatically punishes discounting, or makes independent commercial decision-making less likely.

Competition authorities therefore increasingly focus not only on what an algorithm does, but also on the data supplied to it, who controls it, what information it communicates, and how companies respond to its recommendations.

2. What Is an Algorithmic Compliance Monitoring System?

An algorithmic compliance monitoring system uses predefined rules, statistical techniques, machine learning, or other automated methods to identify conduct that may require legal review.

For example, a company could configure a system to detect:

• unusual similarities between competitors' prices;
• suspicious bidding patterns;
• exchanges of commercially sensitive information;
• resale-price restrictions;
• sudden changes in distributor pricing;
• communications containing cartel-related terminology;
• potentially exclusionary conduct by a dominant company; or
• transactions that may require competition-law approval.

Such monitoring can strengthen compliance because potentially problematic conduct may be detected much earlier than through traditional periodic audits.

The important legal distinction is between monitoring for compliance and monitoring competitors to facilitate coordination.

The first can reduce competition-law risk. The second may itself become evidence of an infringement.

3. Main Competition-Law Issues

A. Price Monitoring

Businesses commonly monitor market prices. Merely observing publicly available prices is not automatically unlawful.

The risk increases where monitoring technology is connected with an agreement or understanding concerning the prices that competitors, suppliers, or distributors should maintain.

An algorithm that immediately identifies a deviation from an agreed price can make a cartel significantly easier to maintain because deviations can be detected and corrected quickly.

B. Exchange of Sensitive Information

Compliance systems sometimes collect information from different market participants.

Particular care is required where the information concerns:

• future prices;
• individual sales;
• production quantities;
• capacity;
• margins;
• customer-specific terms;
• strategic plans; or
• future commercial intentions.

If competitors provide such information to a common algorithm and receive recommendations based on pooled information, authorities may investigate whether the arrangement reduces independent competition.

C. Automated Enforcement of Restrictions

An algorithm may identify companies or distributors that depart from specified prices and automatically trigger warnings or other consequences.

This is especially significant in resale price maintenance cases. A manufacturer generally cannot use monitoring technology as an enforcement mechanism for an unlawful minimum resale-price arrangement.

D. Algorithmic Coordination

Algorithms can increase market transparency. They can observe competitors' behaviour rapidly and respond almost immediately.

Where competitors have already reached an unlawful understanding, algorithms can become extremely effective tools for implementing and policing that understanding.

The difficult question concerns situations where firms use sophisticated systems without an express human agreement. Traditional competition rules generally require an agreement, concerted practice, or other legally recognised form of coordination before ordinary cartel liability arises. Mere parallel conduct is therefore not automatically a cartel.

4. Human Responsibility Cannot Simply Be Delegated to Software

A company generally cannot avoid competition-law responsibility merely by saying:

“The algorithm made the decision.”

Competition law normally examines the conduct of the undertaking using the technology.

Relevant questions include:

1. Who designed or selected the system?
2. What instructions were given to it?
3. What data does it receive?
4. Does it use competitors' confidential information?
5. Does management understand its basic operation?
6. Can employees override its recommendations?
7. Does it monitor adherence to coordinated commercial behaviour?
8. What happens when the system identifies a deviation?

Consequently, algorithmic governance should form part of the company's wider competition compliance programme.

Important Case Laws and Enforcement Examples

There is still limited case law dealing specifically with systems expressly labelled “algorithmic compliance monitoring systems.” The following authorities are important because they establish principles governing algorithms, automated monitoring, common platforms, sensitive information and competition compliance.

5. United States v. David Topkins — United States, 2015

This was an early and influential algorithmic price-fixing prosecution.

David Topkins and other online sellers were accused of agreeing to coordinate prices for certain posters sold through an online marketplace. According to the US Department of Justice, computer code and algorithm-based pricing software were used to implement the agreement.

Topkins agreed to plead guilty and pay a criminal fine.

Importance

The case establishes a straightforward principle:

Using software to implement a traditional cartel does not protect participants from competition law.

An algorithm may simply become the technological mechanism through which an unlawful agreement is implemented.

For compliance monitoring, companies should therefore investigate not merely whether pricing software exists but also why particular rules or parameters were inserted into it.

6. CMA — Trod Limited and GB Eye Limited, 2016

The UK Competition and Markets Authority investigated competing sellers Trod Limited and GB Eye Limited.

The companies agreed that they would not undercut each other's prices for certain posters and frames sold through Amazon's UK marketplace.

Automated repricing software was then used to monitor and adjust prices so that the agreement could operate effectively.

Trod admitted the infringement and received a financial penalty, while GB Eye obtained immunity after reporting the cartel and cooperating with the investigation.

Importance

This case demonstrates the relationship between:

agreement → monitoring → automated implementation.

Monitoring technology can make a cartel more stable because participants can quickly discover whether another participant has departed from their arrangement.

For compliance programmes, unusual algorithmic rules such as “never undercut competitor X” should therefore receive immediate legal review.

7. Eturas UAB and Others v Lithuanian Competition Council — CJEU, Case C-74/14, 2016

Eturas operated an online travel-booking system used by numerous travel agencies.

The dispute concerned a restriction implemented through the common system affecting discounts available through online bookings.

The Court of Justice examined when users of a common electronic platform could be regarded as participating in a concerted practice.

The Court did not establish automatic liability merely because businesses used the same system. Questions of awareness, evidence and distancing from the conduct remained important.

Importance

Eturas is particularly relevant to shared compliance and commercial platforms.

Where multiple competitors use a common system, companies should not assume that decisions implemented through the platform are legally neutral.

A company becoming aware that a shared platform is facilitating potentially anticompetitive coordination should investigate the issue rather than simply allowing the automated arrangement to continue.

8. Samir Agrawal v. Competition Commission of India — Supreme Court of India, 2020

The proceedings concerned algorithmic fares generated by the Ola and Uber platforms.

It was alleged that the pricing algorithms facilitated price fixing among drivers.

The Competition Commission of India found no prima facie infringement, and the litigation ultimately reached the Supreme Court.

The allegation that drivers were effectively participating in a horizontal price-fixing arrangement through the platforms was not accepted on the evidence.

Importance

The case demonstrates an important limitation on algorithmic-collusion theories.

Common use of algorithmic pricing does not automatically prove an agreement between independent market participants.

Competition authorities and courts still have to examine whether the legal requirements for an agreement or concerted practice are satisfied.

This is important for compliance systems because automated similarity in market behaviour should be treated as a warning signal requiring investigation—not automatically as proof of a cartel.

9. CMA — Casio Electronics, Resale Price Maintenance

The UK CMA investigated Casio's restrictions concerning online resale prices for digital pianos and keyboards.

An important feature was Casio's use of price-monitoring software to observe online prices in real time. The monitoring helped identify retailers selling below the prices expected by Casio.

The CMA imposed a substantial financial penalty.

Importance

This case demonstrates that monitoring software itself is not necessarily illegal.

Its purpose and use are critical.

A supplier may legitimately observe market developments, but using automated monitoring as part of a system for maintaining unlawful minimum resale prices can create serious competition-law exposure.

A compliance system should therefore distinguish between:

lawful market intelligence

and

monitoring designed to enforce anticompetitive restrictions.

10. United States v. RealPage Inc. — Algorithmic Rental Pricing Litigation

The US Department of Justice brought proceedings against RealPage concerning revenue-management software used in rental housing.

The government's allegations focused particularly on the use of non-public, competitively sensitive information supplied by competing landlords and its incorporation into pricing recommendations.

The litigation became one of the most important modern examples of competition authorities examining shared algorithmic infrastructure.

In November 2025, the DOJ announced a proposed settlement with RealPage. Among other measures, the proposed terms restricted the use of competitors' non-public competitively sensitive information in determining rental prices and required changes to features alleged to align competitors' pricing. A court-appointed compliance monitor was also contemplated.

Importance

RealPage illustrates a major modern compliance principle:

The architecture of the data matters as much as the algorithm.

A compliance review should therefore determine:

• where algorithmic inputs originate;
• whether competitors contributed them;
• whether the information is public or confidential;
• how recent the information is;
• whether individual competitors can be identified; and
• whether outputs influence commercially sensitive decisions.

11. US Proceedings Involving Greystar and Other Landlords

The RealPage litigation expanded to allegations involving several major property managers.

In 2025, the DOJ announced a proposed settlement with Greystar concerning algorithmic coordination and exchanges of competitively sensitive information. The proposed restrictions included limitations on sharing sensitive information and safeguards concerning third-party pricing algorithms.

Related proceedings and proposed settlements have continued to develop, including action involving other landlords.

Importance

These proceedings demonstrate that competition authorities may examine both:

the algorithm provider

and

businesses using the algorithm.

A company therefore cannot assume that competition compliance is entirely the software provider's responsibility.

Users should conduct their own antitrust assessment of third-party pricing and decision-making systems.

12. Booking.com BV v 25hours Hotel Company Berlin GmbH — CJEU, Case C-264/23, 2024

Although this case was not specifically about compliance-monitoring algorithms, it is important to digital competition governance.

The case concerned price-parity clauses imposed on accommodation providers using Booking.com's platform.

The Court of Justice held that such parity clauses could not, in principle, simply be classified as ancillary restraints necessary for the operation of the platform.

Importance

Algorithmic compliance systems should therefore monitor contractual restrictions as well as automated conduct.

Digital businesses should review arrangements such as:

• parity clauses;
• exclusivity requirements;
• platform restrictions;
• preferential treatment;
• access conditions; and
• automated enforcement of contractual restrictions.

Technology does not change the fundamental requirement that these restrictions must comply with competition law.

13. How an Effective Algorithmic Competition Compliance System Should Work

A properly designed system can operate in several stages.

Stage 1 — Data Collection

The system collects relevant internal information concerning pricing, sales, contracts, communications and commercial decisions.

Collection should be proportionate and subject to appropriate access controls.

Stage 2 — Competition Risk Detection

Algorithms can identify unusual patterns such as:

• identical competitor pricing following communications;
• suspiciously stable market shares;
• coordinated bidding patterns;
• repeated contacts with competitors before pricing decisions;
• systematic enforcement of minimum resale prices; or
• unusual exchanges of commercially sensitive information.

Stage 3 — Human Review

A flagged transaction should generally be reviewed by qualified personnel.

Algorithms can produce false positives. Similar prices, for example, can result from legitimate competition rather than collusion.

Human legal analysis therefore remains essential.

Stage 4 — Escalation

Higher-risk matters should be escalated to appropriate compliance or legal personnel.

The system should preserve relevant evidence and prevent inappropriate deletion or alteration of records.

Stage 5 — Remediation

Where a genuine problem is discovered, possible responses include stopping the conduct, modifying the algorithm, restricting particular data inputs, improving employee training and obtaining specialist legal advice.

14. Important Compliance Safeguards

Businesses using algorithmic monitoring should maintain clear governance.

Independent pricing: Competitors should continue making genuinely independent commercial decisions.

Sensitive-data controls: Competitor-specific confidential information should not enter shared systems without careful legal assessment.

Algorithm documentation: Businesses should understand and document the objectives, inputs and important parameters of significant commercial algorithms.

Human oversight: High-risk outputs should be capable of review and challenge by appropriately trained personnel.

Regular auditing: Systems should be tested to determine whether their practical operation differs from their intended design.

Vendor assessment: Companies using third-party algorithms should investigate how the vendor collects information and generates recommendations.

Change management: Material modifications to pricing or commercial algorithms should trigger renewed competition-law review.

15. Advantages of Algorithmic Compliance Monitoring

When properly designed, algorithmic systems can improve competition compliance considerably.

They can process enormous quantities of information, identify suspicious patterns earlier, create systematic audit trails and allow compliance teams to concentrate on higher-risk activity.

They can therefore transform competition compliance from an occasional investigation into a more continuous process.

However, automated monitoring should support rather than completely replace legal judgment.

16. Main Risks

The biggest risk is that a system designed for compliance gradually becomes a commercial coordination mechanism.

For example, a monitoring system may initially identify unusual competitor prices. If it is subsequently programmed automatically to match those prices, punish distributors that discount, or distribute competitors' confidential information, its legal character may change significantly.

There are also risks involving false positives, poor-quality data, unexplained machine-learning decisions and excessive reliance on software recommendations.

For that reason, algorithmic compliance requires both technical governance and competition-law governance.

17. Conclusion

Algorithmic compliance monitoring systems can be powerful tools for detecting competition-law problems, but algorithms are legally relevant according to how they are designed and used.

Cases such as Topkins, Trod/GB Eye, Eturas, Samir Agrawal v CCI, Casio, RealPage, the related US landlord proceedings, and Booking.com demonstrate different parts of the developing legal framework.

The central principles are clear:

Automation does not legalise an unlawful agreement.

Common use of an algorithm does not, by itself, establish collusion.

Competitively sensitive data supplied to shared systems requires particular scrutiny.

Monitoring technology must not become a mechanism for enforcing price fixing or resale price maintenance.

Businesses remain responsible for competition compliance when commercial decisions are delegated to algorithms.

Accordingly, an effective algorithmic compliance system should combine automated detection with data controls, documented governance, independent commercial decision-making and meaningful human legal review.

I’ve treated “case laws” broadly enough to include major judicial decisions and significant competition-authority enforcement matters, because dedicated judgments specifically about compliance-monitoring algorithms remain limited. The authorities and current RealPage developments were checked against court/government or competition-authority materials as of September 2026.