Competition Law and Regulatory Technology in Competition Enforcement

Introduction

Regulatory Technology (RegTech) refers to the use of technology—such as artificial intelligence, machine learning, big-data analytics, automated screening, natural-language processing, network analysis, and digital evidence tools—to improve regulatory compliance, detection, investigation, and enforcement.

In competition law enforcement, RegTech can help competition authorities detect cartels, identify exclusionary conduct, analyse mergers, monitor digital markets, process large quantities of electronic evidence, and detect algorithmically coordinated conduct. At the same time, technological enforcement creates legal concerns concerning due process, transparency, explainability, privacy, evidentiary reliability, false positives, confidentiality, and judicial review.

RegTech therefore changes not only how competition authorities enforce the law, but potentially what kinds of conduct they are capable of detecting.

I. Meaning and Scope of RegTech in Competition Enforcement

Competition authorities traditionally depend upon:

• complaints;
• leniency applications;
• dawn raids;
• witness testimony;
• documentary evidence;
• economic analysis;
• market studies; and
• merger notifications.

RegTech supplements these methods through automated or semi-automated systems.

Major applications

1. Cartel detection

Authorities can analyse:

• bid prices;
• tender histories;
• winning-bid patterns;
• market allocation;
• communication networks;
• identical price movements;
• suspicious bidding rotations;
• unusual withdrawal patterns.

Algorithms can identify combinations of indicators that would be difficult to detect manually.

2. Digital-market monitoring

RegTech can continuously monitor:

• platform ranking;
• search results;
• product visibility;
• commissions;
• pricing;
• app-store restrictions;
• interoperability;
• self-preferencing;
• tying;
• data access; and
• algorithmic changes.

This is particularly important because digital markets can change much faster than conventional regulatory investigation cycles.

3. Merger screening

Automated systems may assist authorities in identifying:

• potentially problematic acquisitions;
• killer acquisitions;
• acquisitions of nascent competitors;
• serial acquisitions;
• overlapping digital ecosystems;
• concentration in data markets; and
• acquisitions below conventional notification thresholds.

4. Evidence discovery

Natural-language-processing systems can analyse millions of:

• emails;
• instant messages;
• contracts;
• spreadsheets;
• internal reports;
• source documents; and
• electronic communications.

This can substantially reduce the time required for document review.

5. Market surveillance

Authorities can create automated dashboards to monitor:

prices → market shares → supply conditions → consumer complaints → competitor behaviour → algorithmic changes

This moves enforcement toward continuous competition monitoring rather than purely reactive investigation.

II. Legal Framework

RegTech does not create an independent competition-law offence. Rather, it is an enforcement methodology operating within existing competition statutes.

Depending upon the jurisdiction, the underlying legal provisions may concern:

• anti-competitive agreements;
• cartels;
• abuse of dominance;
• monopolisation;
• restrictive trade practices;
• merger control;
• bid rigging;
• information exchange;
• exclusionary conduct; and
• consumer harm.

The technology must therefore remain subordinate to the legal test.

For example:

Algorithmic identification of suspicious pricing ≠ proof of a cartel.

The authority still has to establish the legally relevant elements of the infringement.

III. RegTech and Cartel Enforcement

Cartels are particularly suitable for technological detection because collusion may produce identifiable statistical patterns.

Examples of indicators

An enforcement system could flag:

1. unusually stable market shares;
2. repeated bid rotation;
3. identical or near-identical bids;
4. suspicious price parallelism;
5. geographically patterned winning bids;
6. simultaneous price increases;
7. unusual capacity allocation;
8. communications between competitors; and
9. unexplained deviations from competitive pricing.

However, parallel conduct alone is generally insufficient to establish unlawful coordination.

This distinction is essential because competitive firms may independently respond to:

• common costs;
• inflation;
• supply shortages;
• market shocks;
• publicly available information; or
• common demand conditions.

IV. Algorithmic Collusion and RegTech

A particularly difficult issue is algorithmically coordinated markets.

Two competing businesses may independently use pricing algorithms. The algorithms might:

• observe competitors' prices;
• respond automatically to market changes;
• learn optimal pricing strategies; and
• repeatedly adjust prices.

The resulting market may display coordinated pricing without an express human agreement.

Competition authorities therefore increasingly need technological tools capable of determining:

whether observed coordination results from lawful independent adaptation or unlawful coordination.

RegTech may assist by examining:

• algorithmic decision rules;
• training data;
• pricing histories;
• communication between firms;
• changes in algorithmic parameters;
• patterns of deviations;
• internal instructions; and
• algorithmic responses to competitors.

V. RegTech in Abuse-of-Dominance Investigations

RegTech can also identify potential exclusionary conduct by dominant undertakings.

Examples

A platform may allegedly:

• favour its own products;
• demote competitors;
• impose discriminatory access conditions;
• restrict interoperability;
• tie services;
• impose exclusivity;
• discriminate in rankings;
• restrict data portability; or
• use commercially sensitive data obtained from rivals.

Automated systems can compare millions of transactions and ranking decisions.

For example, a competition authority might analyse:

1 million search queries → competitor visibility → platform-owned product visibility → consumer click-through rates → conversion rates.

This can help establish whether a particular technical design produces measurable competitive effects.

VI. RegTech and Merger Control

Traditional merger control focuses heavily on:

• turnover;
• market shares;
• concentration;
• barriers to entry;
• efficiencies;
• unilateral effects; and
• coordinated effects.

RegTech can supplement this analysis through automated data collection.

It can identify:

• acquisitions of emerging competitors;
• repeated acquisitions by large technology companies;
• overlapping datasets;
• ecosystem effects;
• common ownership patterns;
• vertical relationships; and
• acquisitions that may escape traditional thresholds.

This is particularly relevant where a transaction's competitive significance is greater than its immediate turnover suggests.

VII. Six Important Case Laws

1. United States v. Apple Inc. — e-books

United States v. Apple Inc., 791 F.3d 290 (2d Cir. 2015)

The case concerned Apple's alleged role in facilitating coordination among major publishers concerning e-book pricing.

The Second Circuit upheld findings against Apple under U.S. antitrust law.

RegTech relevance

The case demonstrates the importance of analysing:

• communications;
• contractual arrangements;
• timing of pricing changes;
• relationships among competitors; and
• market-wide pricing patterns.

Modern RegTech can combine these different datasets and identify relationships that may otherwise remain dispersed across thousands of documents.

Principle

Technological evidence can assist in demonstrating the factual structure surrounding coordinated conduct, but the legal infringement still depends upon the applicable antitrust test.

2. United States v. Airline Tariff Publishing Co.

United States v. Airline Tariff Publishing Co., 836 F. Supp. 9 (D.D.C. 1993)

Airlines used an electronic tariff publishing system through which information concerning proposed fares could be communicated rapidly.

The case illustrated how competitors could use information technology to facilitate coordination.

RegTech relevance

It is particularly important for understanding the relationship between:

information systems + transparency + rapid communication + coordination risk.

Modern enforcement systems can monitor digital pricing environments much more extensively than was possible in the early electronic-information era.

Principle

Technology may lower the practical barriers to coordination, making technologically sophisticated monitoring increasingly important.

3. United States v. Topkins

United States v. Topkins, No. CR 15-00227 (N.D. Cal. 2015)

This prosecution involved allegations concerning an agreement among online sellers to fix prices of posters and frames.

The case is significant because pricing algorithms and software were associated with the implementation of the pricing strategy.

RegTech relevance

It demonstrates the importance of distinguishing between:

A. technology used by humans to implement an agreement, and

B. independent algorithms that produce coordination without an explicit human agreement.

In the first situation, the technology may simply be the mechanism through which conventional cartel conduct is implemented.

Principle

The use of an algorithm does not make otherwise unlawful coordination lawful.

4. Eturas UAB v. Lietuvos Respublikos konkurencijos taryba

Case C-74/14, Eturas UAB v. Lietuvos Respublikos konkurencijos taryba (CJEU, 2016)

The case concerned an electronic travel-booking system through which a technical restriction was introduced concerning discounts.

The Court addressed the evidentiary significance of participation in a technological system associated with potentially coordinated conduct.

RegTech relevance

Eturas is particularly valuable for modern digital enforcement because it demonstrates that competition authorities may have to analyse:

• electronic platforms;
• automated technical changes;
• system-wide communications;
• users' awareness;
• technical implementation; and
• subsequent conduct.

Principle

Digital infrastructure can constitute an important factual component in establishing whether undertakings participated in coordinated conduct.

5. AC-Treuhand AG v European Commission

AC-Treuhand AG v European Commission, Case C-194/14 P (CJEU, 2015)

The case concerned the liability of a consultancy that facilitated cartel activity without itself being a conventional competitor in the relevant product market.

RegTech relevance

The case illustrates the importance of examining the ecosystem surrounding a cartel, rather than looking exclusively at competing producers.

Modern analytical systems can map:

• consultants;
• intermediaries;
• trade associations;
• communication channels;
• data providers; and
• other facilitating actors.

Principle

Competition enforcement may need to examine the network of participants and facilitators surrounding anti-competitive coordination.

6. Google Shopping

Google Search (Shopping), Case AT.39740, European Commission decision (2017); General Court judgment, Case T-612/17 (2021)

The case concerned Google's treatment of its own comparison-shopping service in search results.

The European Commission found that Google had abused its dominant position by giving prominent placement to its own comparison-shopping service while applying less favourable positioning and display to competing comparison-shopping services.

RegTech relevance

This is highly significant for technological enforcement because the alleged conduct involved:

• search algorithms;
• ranking;
• visibility;
• traffic;
• click-through behaviour;
• large-scale data analysis; and
• dynamic digital markets.

A modern RegTech system can continuously analyse ranking outcomes and detect systematic differences in treatment.

Principle

Competition enforcement in digital markets may require examination of the technical operation and practical effects of algorithms, rather than merely contractual terms.

VIII. Additional Relevant Cases

7. Google Android

Google Android, Case AT.40099, European Commission (2018); General Court, Case T-604/18 (2022)

The proceedings concerned Google's contractual practices involving Android devices, including restrictions concerning search and application distribution.

RegTech relevance: automated analysis can identify technical and contractual relationships between operating systems, app stores, search services, and competing applications.

8. Google AdSense

Google Search (AdSense), Case AT.40411, European Commission (2019)

The Commission examined contractual restrictions relating to online search advertising intermediation.

RegTech relevance: advertising markets generate huge datasets, making automated analysis particularly relevant to identifying exclusionary restrictions and market effects.

9. Booking.com / hotel-booking practices

European competition authorities have investigated hotel-booking platform practices involving parity clauses and platform relationships.

RegTech relevance: automated monitoring can compare prices across platforms and identify potential effects of contractual restrictions.

IX. RegTech and Evidentiary Problems

Technology creates an important legal distinction:

Detection is not proof.

An algorithm may identify a suspicious pattern, but enforcement authorities still need reliable evidence.

Problems include:

1. False positives

An algorithm may identify conduct as suspicious when it is actually competitive.

2. False negatives

Sophisticated collusion may deliberately avoid patterns detectable by conventional algorithms.

3. Explainability

A business must be able to understand why conduct has been considered suspicious, particularly when enforcement action follows.

4. Data quality

Incomplete or biased datasets can distort enforcement conclusions.

5. Algorithmic bias

The enforcement system itself may produce systematic errors.

6. Reproducibility

The authority should be able to demonstrate how an analytical result was generated.

7. Confidentiality

Competition investigations frequently involve:

• trade secrets;
• commercially sensitive information;
• personal communications; and
• privileged material.

X. Due Process and Procedural Fairness

The use of RegTech must respect procedural safeguards.

An undertaking should generally have meaningful opportunities to:

• know the allegations;
• inspect relevant evidence subject to lawful confidentiality restrictions;
• challenge the authority's interpretation;
• contest technical methodology;
• present economic evidence;
• challenge unreliable data; and
• obtain judicial review.

A particularly important issue arises where an authority relies heavily upon a proprietary or opaque algorithm.

If the undertaking cannot meaningfully challenge the analytical methodology, questions may arise concerning:

• equality of arms;
• effective defence;
• transparency;
• reasoned decision-making; and
• judicial review.

XI. Privacy and Data Protection

RegTech can involve extensive data collection.

Competition authorities may process:

• emails;
• employee communications;
• transaction records;
• customer data;
• location information;
• online behaviour; and
• business records.

Therefore, competition enforcement must coexist with applicable:

• privacy legislation;
• data-protection requirements;
• confidentiality obligations;
• legal privilege;
• cybersecurity standards.

The enforcement objective does not automatically eliminate these protections.

XII. RegTech and Dawn Raids

Traditional dawn raids involve physical seizure and review of documents.

Modern investigations increasingly involve:

physical devices → cloud accounts → messaging applications → databases → metadata → collaboration platforms.

RegTech can help authorities:

• identify relevant documents;
• remove duplicates;
• classify documents;
• detect communication networks;
• identify relevant keywords;
• reconstruct timelines.

This dramatically increases investigative capacity.

However, over-inclusive automated collection can raise proportionality and privilege concerns.

XIII. Real-Time Competition Monitoring

One of the most significant developments is the movement from periodic investigation to continuous monitoring.

Traditional model:

Complaint → Investigation → Evidence collection → Decision

RegTech model:

Continuous data → Automated anomaly detection → Human review → Investigation → Enforcement

This may be especially valuable in:

• digital advertising;
• online marketplaces;
• financial technology;
• airline pricing;
• online retail;
• app stores;
• energy markets; and
• telecommunications.

XIV. Human Oversight

RegTech should generally operate as a decision-support mechanism, rather than an autonomous adjudicator.

A useful institutional model is:

Stage 1 — Automated screening

Technology identifies unusual conduct.

↓

Stage 2 — Human verification

Investigators assess whether the anomaly has a legitimate explanation.

↓

Stage 3 — Economic analysis

Economists examine market conditions and competitive effects.

↓

Stage 4 — Legal analysis

Lawyers determine whether statutory elements are satisfied.

↓

Stage 5 — Procedural safeguards

The undertaking receives appropriate opportunities to respond.

↓

Stage 6 — Final decision

The competent authority makes the legal determination.

This model reduces the risk of allowing statistical correlation to become a substitute for legal proof.

XV. Benefits of RegTech

XVI. Risks of RegTech

XVII. RegTech and the Standard of Proof

A central principle should be:

The more consequential the enforcement decision, the greater the need for human-verifiable evidence supporting the technological inference.

For example:

Automated alert:
“Five firms increased prices simultaneously.”

This is an investigative lead.

It is not necessarily proof of:

“The firms entered into an unlawful agreement.”

Investigators may need additional evidence such as:

• communications;
• meetings;
• contractual arrangements;
• admissions;
• internal documents;
• economic evidence; or
• other indicia of coordination.

XVIII. Regulatory Technology and Competition Compliance

RegTech is useful not only to competition authorities but also to businesses.

Companies can use compliance systems to monitor:

• employee communications;
• competitor contacts;
• pricing algorithms;
• bid submissions;
• distribution agreements;
• exclusivity provisions;
• information exchange;
• merger integration;
• sensitive information access.

A competition-compliance system could generate an internal alert when an employee attempts to share:

competitor prices + future pricing intentions + commercially sensitive information.

This can prevent violations before they occur.

XIX. RegTech and the Future of Competition Enforcement

The future enforcement architecture is likely to combine:

AI + big data + economic modelling + network analysis + human investigators + judicial oversight.

Three developments are particularly important.

1. Predictive enforcement

Authorities may identify markets showing elevated competition risks before receiving a complaint.

2. Algorithm auditing

Authorities may increasingly examine whether pricing, ranking, recommendation, or allocation algorithms produce exclusionary or discriminatory outcomes.

3. Continuous regulatory supervision

Digital platforms may increasingly be monitored through automated systems capable of detecting significant changes in:

• ranking;
• pricing;
• access;
• interoperability;
• commissions;
• consumer switching;
• market shares.

XX. Key Legal Principles Emerging from the Case Law

The cases discussed above collectively demonstrate several important principles:

1. Technology can facilitate conventional cartel conduct.
2. Electronic communications can constitute important evidence of coordination.
3. Algorithms do not immunise anti-competitive conduct from competition law.
4. Digital platforms can create novel forms of exclusionary conduct.
5. Automated detection must be distinguished from legal proof.
6. Competition authorities must preserve procedural fairness when using sophisticated analytical tools.
7. Human assessment remains important when technological evidence is ambiguous.
8. Digital-market enforcement increasingly requires technical understanding of platform architecture and algorithms.

Conclusion

Regulatory Technology is transforming competition enforcement from a predominantly reactive, document-driven process into a potentially continuous, data-driven system. It can help authorities detect cartels, monitor digital platforms, analyse algorithms, screen mergers, process massive electronic evidence, and identify emerging competition problems.

However, RegTech should not replace the legal judgment of competition authorities. The central distinction remains:

Detection → investigation → evidence → legal analysis → adjudication.

An algorithm may identify a competition concern, but the existence of an algorithmic anomaly does not itself establish an infringement.

The most appropriate framework is therefore one in which RegTech functions as an advanced investigative and supervisory instrument, combined with human expertise, transparent methodologies, reliable evidence, proportionality, confidentiality protections, and effective judicial review.

Short Case-Law List for Examination

1. United States v. Apple Inc., 791 F.3d 290 (2d Cir. 2015) — electronic coordination and e-books.
2. United States v. Airline Tariff Publishing Co., 836 F. Supp. 9 (D.D.C. 1993) — electronic pricing information and coordination.
3. United States v. Topkins, No. CR 15-00227 (N.D. Cal. 2015) — algorithm-assisted online price fixing.
4. Eturas UAB v. Lietuvos Respublikos konkurencijos taryba, Case C-74/14 (CJEU, 2016) — electronic platform and coordinated conduct.
5. AC-Treuhand AG v European Commission, Case C-194/14 P (CJEU, 2015) — facilitation of cartel activity.
6. Google Search (Shopping), Case AT.39740 / Case T-612/17 — algorithmic ranking and self-preferencing.
7. Google Android, Case AT.40099 / Case T-604/18 — digital ecosystem restrictions.
8. Google AdSense, Case AT.40411 — digital advertising intermediation and exclusionary restrictions.