Civil Law And Algorithmic Insider Trading Detection Error Claims In Europe

1. Introduction

Algorithmic insider-trading detection refers to the use of automated or AI-based systems by financial institutions, trading venues, regulators, exchanges, brokers, and compliance departments to identify potentially suspicious trading.

These systems may analyse:

trading patterns;

order timing;

cancellations;

communications;

telephone records;

transaction history;

relationships between traders;

unusual price movements;

access to inside information;

insider lists;

market rumours;

account relationships;

statistically unusual behaviour.

An algorithmic insider-trading detection error claim arises when an automated system incorrectly identifies legitimate activity as suspicious, or fails to identify genuine insider dealing, and that error causes legal, financial, reputational, regulatory, contractual, or other civil harm.

European law does not yet contain a single body of case law specifically called “algorithmic insider-trading detection error.” The relevant legal framework is instead built from EU Market Abuse Regulation (MAR) jurisprudence, data-protection law, communications-privacy law, financial-services regulation, fundamental rights and national civil/procedural law.

A particularly important point is that many European insider-trading cases are regulatory or criminal proceedings rather than ordinary civil lawsuits. They nevertheless provide principles directly relevant to civil claims concerning defective algorithmic detection.

2. What Is Algorithmic Insider-Trading Detection?

A simplified system may operate as follows:

Trading data

↓

Algorithm/AI system

↓

Pattern detection

↓

Risk score

↓

Suspicious transaction/order alert

↓

Human compliance investigation

↓

Suspicious Transaction and Order Report (STOR) / regulatory investigation

↓

Administrative, civil or criminal proceedings

The legal difficulty occurs when one of the earlier stages is wrong.

For example:

A legitimate trade is identified by an algorithm as a 97% probability of insider dealing.

The compliance department relies on the score.

The trader is reported to the regulator.

The regulator investigates.

The trader suffers:

suspension;

loss of employment;

reputational damage;

legal expenses;

trading restrictions;

business losses.

The trader may then argue:

The algorithm was defective and the resulting regulatory action was based on unreliable evidence.

3. European Regulatory Framework

The principal instrument is Regulation (EU) No 596/2014 — Market Abuse Regulation (MAR).

MAR addresses:

insider dealing;

unlawful disclosure of inside information;

market manipulation;

prevention and detection of market abuse;

suspicious transaction/order reporting;

supervisory powers.

ESMA explains that persons professionally arranging or executing transactions must maintain systems and procedures for detecting and reporting suspicious orders and transactions. The obligation can extend beyond traditional MiFID investment firms to other professionally arranging or executing entities. (ESMA)

The regulatory system therefore assumes that sophisticated technological monitoring will be used.

That creates a corresponding legal question:

What happens when the monitoring system itself produces an error?

4. Types of Algorithmic Detection Errors

A. False positive

The algorithm identifies lawful trading as insider trading.

Example:

Normal pre-announcement trading → algorithmic alert → false suspicion.

This is potentially the most important issue for civil claims.

B. False negative

The algorithm fails to identify actual insider dealing.

Example:

Insider trades before a confidential announcement → algorithm does not detect the pattern.

Possible consequences include:

investor losses;

regulatory criticism;

liability of the compliance institution;

supervisory sanctions.

C. Data error

Incorrect or incomplete data are fed into the algorithm.

For example:

incorrect insider list;

wrong timestamps;

missing transactions;

inaccurate communication records.

D. Model error

The model itself is defective.

Examples:

inappropriate statistical assumptions;

poorly selected variables;

excessive sensitivity;

inadequate validation;

outdated training data.

E. Context error

The algorithm correctly detects unusual behaviour but incorrectly interprets its meaning.

For example:

Employee trades shortly before announcement.

The algorithm identifies unusual timing.

But the employee may have traded because of an independent investment strategy unrelated to inside information.

F. Correlation error

The algorithm finds a statistical relationship but mistakes it for evidence of insider dealing.

Correlation is not necessarily proof of unlawful conduct.

5. Case Law 1 — Spector Photo Group, C-45/08

Court

CJEU

Date

23 December 2009

Importance

Foundational European insider-dealing authority.

In Spector Photo Group and Van Raemdonck, the CJEU interpreted the EU insider-dealing regime concerning transactions undertaken while possessing inside information.

The Court emphasised that the insider-dealing prohibition protects the integrity of financial markets and aims to prevent persons possessing inside information from obtaining an advantage over investors who do not possess that information. (Infocuria)

Relevance to algorithms

An algorithm might identify:

possession of information + transaction + price movement

and assign a high probability of insider dealing.

But the algorithmic correlation cannot itself replace the legal analysis required to establish whether the relevant elements of insider dealing are present.

The system must distinguish:

possession of information;

nature of the information;

transaction;

knowledge;

circumstances;

possible legitimate explanations.

Principle

An algorithmic alert is an investigative signal, not automatically proof of insider dealing.

6. Case Law 2 — Geltl v Daimler, C-19/11

Court

CJEU

Date

28 June 2012

Importance

Definition of precise inside information.

The case concerned information relating to an intermediate stage in a prolonged corporate process.

The CJEU interpreted the concept of “precise” information and explained that information can qualify as inside information where there is a reasonable expectation concerning circumstances or an event and the information is sufficiently specific to permit conclusions about its potential price effect. (Infocuria)

Relevance to algorithmic detection

An algorithm may have difficulty with intermediate events.

For example:

negotiations → preliminary agreement → board approval → final transaction.

The algorithm may classify every unusual trade occurring during this period as suspicious.

But legally, the system must distinguish between:

information that actually qualifies as inside information;

preliminary information;

rumours;

speculation;

information that lacks sufficient precision.

Principle

An algorithm must not convert every statistically unusual trade into an insider-trading conclusion without analysing the legal status of the underlying information.

7. Case Law 3 — Lafonta v Autorité des marchés financiers, C-628/13

Court

CJEU

Date

11 March 2015

The CJEU held that information can be “precise” even when its holder does not know exactly how it will affect the price of the relevant financial instrument.

The Court rejected an approach under which uncertainty concerning the precise direction or magnitude of the price movement would automatically prevent information from being inside information. (curia)

Algorithmic significance

An algorithm may be trained to look for:

information → expected price movement.

But the legal test is not necessarily:

“Can the algorithm predict the exact price movement?”

The legal concept of inside information can exist even when the exact price effect is uncertain.

Error risk

An overly rigid algorithm could produce:

False negative:

“Price impact uncertain → not inside information.”

That could be legally incorrect.

Principle

Predictive uncertainty should not automatically be treated as absence of inside information.

8. Case Law 4 — Autorité des marchés financiers, C-302/20

Court

CJEU Grand Chamber

Date

15 March 2022

This case concerned a journalist who disclosed information concerning the forthcoming publication of articles reporting market rumours concerning possible takeover bids.

The CJEU examined:

inside information;

precision;

disclosure;

journalism;

freedom of expression;

proportionality.

The Court recognised that information concerning the forthcoming publication of a press article can potentially constitute inside information, but the assessment requires attention to the particular circumstances. (curia)

Algorithmic importance

This illustrates the danger of category-based automated classification.

An algorithm may classify:

“Journalist + market rumour + pre-publication trade = insider dealing.”

But the legal analysis may require consideration of:

the content of the information;

its precision;

how it was obtained;

whether disclosure was lawful;

journalistic activity;

freedom of expression;

proportionality.

Principle

Context matters; algorithmic categorisation cannot automatically replace legal qualification.

9. Case Law 5 — Joined Cases VD and SR, C-339/20 and C-397/20

Court

CJEU Grand Chamber

Date

20 September 2022

This is one of the most important cases for algorithmic detection systems because it concerns the data used to detect insider dealing.

The French authorities sought access to telecommunications traffic data in investigations concerning market abuse.

The CJEU held that EU law precludes legislation providing for general and indiscriminate retention of traffic data for the purpose of combating market-abuse offences. (EUR-Lex)

The Court emphasised that traffic data can be crucial—and sometimes the only—evidence capable of identifying contacts and relationships relevant to insider dealing or market manipulation. (EUR-Lex)

But that investigative importance does not eliminate fundamental-rights safeguards.

The case involved:

privacy;

communications confidentiality;

personal data;

proportionality;

effective investigation;

evidence.

Algorithmic significance

An insider-trading algorithm may analyse:

telephone metadata;

communication timing;

contact networks;

trading times;

relationships between persons.

If the underlying data were unlawfully obtained or retained, the legality and evidential value of the algorithmic conclusion may become contested.

Importantly, the CJEU stated that the admissibility of evidence obtained contrary to EU law is generally governed by national law, subject to the principles of equivalence and effectiveness. (EUR-Lex)

Principle

A sophisticated detection algorithm cannot cure an unlawful evidence-acquisition process.

10. Case Law 6 — Consob, C-481/19

Court

CJEU Grand Chamber

Date

2 February 2021

This case concerned insider-dealing sanctions imposed by the Italian financial regulator and the individual's refusal to answer questions that could expose him to liability.

The CJEU considered the right to silence and protection against self-incrimination under Articles 47 and 48 of the EU Charter. (curia)

Relevance to algorithmic detection

Suppose an algorithm produces:

“High probability of insider dealing.”

The regulator then uses that result to interrogate the person.

The person should not automatically lose procedural protections merely because the initial suspicion originated from an automated system.

Algorithmic detection therefore does not eliminate:

right to silence;

defence rights;

procedural fairness;

ability to challenge the investigation.

Principle

Algorithmic suspicion does not eliminate fundamental procedural rights.

11. Case Law 7 — Georgakis, C-391/04

Court

CJEU

Date

10 May 2007

The case concerned coordinated stock-market transactions designed to support artificially the price of securities.

The CJEU examined the meaning of taking advantage of inside information and coordinated transactions among persons possessing relevant information. (Infocuria)

Algorithmic importance

This demonstrates the difficulty of detecting collective or coordinated conduct.

An algorithm may see:

unusual simultaneous transactions;

linked accounts;

coordinated orders.

But the legal assessment requires distinguishing:

legitimate coordinated trading

from

conduct constituting prohibited market abuse.

Principle

Network correlation is evidence requiring legal interpretation, not automatic proof of unlawful coordination.

12. Case Law 8 — Finansinspektionen v Carnegie Investment Bank, C-363/24

Court

CJEU

Date

19 March 2026

This is a particularly useful recent authority.

The case concerned an insider list and information concerning a person's inclusion on that list.

The CJEU confirmed that information concerning inclusion on an insider list and a restriction on selling shares can itself potentially constitute information of a precise nature, provided the legal conditions are satisfied. The Court also explained that information that later turns out to be incorrect can still qualify as inside information if, at the relevant time, it was credible and capable of providing an economic advantage. (Curia)

Importance for algorithmic error claims

This case is particularly useful for understanding the distinction between:

information that turns out to be wrong

and

information that was legally unreliable at the time.

An algorithm may retrospectively be shown to have made an incorrect prediction.

That does not automatically answer whether the underlying information was legally relevant at the time.

Principle

Ex-post falsity and ex-ante unreliability are not necessarily the same legal question.

13. The Core Problem: False Positives

Suppose an algorithm identifies 1,000 suspicious trades.

Later investigation establishes:

700 were legitimate;

200 were uncertain;

100 were genuine insider-trading cases.

The system has produced a substantial number of false positives.

This can create several legal questions:

Against the algorithm provider

Was the system defective?

Against the financial institution

Was the system negligently implemented?

Against compliance personnel

Was there unreasonable reliance on the automated output?

Against the regulator

Was the regulatory decision based upon adequate evidence?

Under data-protection law

Was personal data processed lawfully?

Under procedural law

Was the affected person given an adequate opportunity to challenge the allegation?

14. False Negatives

The opposite problem also exists.

Suppose:

10 genuine insider transactions occurred.

The system detects only 2.

Potential consequences include:

investors suffering losses;

regulatory penalties;

supervisory action;

contractual claims;

professional-negligence claims;

possible liability under national law.

A compliance institution cannot necessarily argue:

“The computer did not detect it, therefore we had no responsibility.”

The regulatory framework expects systems and procedures capable of detecting suspicious activity. ESMA states that persons professionally arranging or executing transactions must implement arrangements, systems and procedures for detection and reporting. (ESMA)

15. Algorithmic Detection Is Not Proof

This is the central principle.

Consider:

Algorithmic score: 96% suspicious

That does not necessarily mean:

96% probability that the person legally committed insider dealing.

The score could represent:

unusual trading;

correlation with announcements;

similarity to historical cases;

unusual order timing;

communication overlap.

These are indicators, not necessarily legal conclusions.

The legal question remains whether the elements of insider dealing are established.

16. The Four Elements of Inside Information

Under MAR Article 7, inside information has four central characteristics:

Precise nature

Non-public

Direct or indirect relation to financial instruments or their issuer

Potential significant price effect

The CJEU reaffirmed these elements in its recent Finansinspektionen judgment. (Curia)

An algorithm should therefore not be designed around a simplistic formula such as:

unusual trade = insider trading.

It should assist the investigation of these legal elements.

17. Algorithmic Detection and Data Quality

The quality of algorithmic detection depends heavily upon data.

Potential errors include:

incorrect timestamps;

missing trades;

duplicate trades;

incorrect account ownership;

incomplete insider lists;

incorrect employee records;

inaccurate communication metadata;

incorrect corporate-event dates.

A simple chain illustrates the problem:

Incorrect data

↓

Incorrect model input

↓

Incorrect risk score

↓

Incorrect suspicion

↓

Incorrect investigation

↓

Potential harm

Thus, an error claim may concern the data pipeline, rather than the AI model itself.

18. Model Validation

A financial institution using an algorithmic detection system should be able to address questions such as:

Was the model independently validated?

What is its false-positive rate?

What is its false-negative rate?

How frequently is it tested?

How often is it recalibrated?

Does performance differ across financial instruments?

Does it work in volatile markets?

Does it work during corporate announcements?

Has model drift occurred?

ESMA's technical framework for market-abuse detection contemplates systems capable of analysing transactions and orders and generating alerts requiring further analysis. The systems should be appropriate and proportionate to the scale and nature of the activity. (ESMA)

19. Human Review

A crucial safeguard is the distinction between:

Automated alert

“This trade requires investigation.”

and

Automated legal conclusion

“This person committed insider dealing.”

The first is much easier to justify.

The second raises considerably greater legal concerns.

The human reviewer should examine:

the transaction;

relevant information;

timing;

insider status;

legitimate explanations;

communications;

market circumstances;

other evidence.

20. Automation Bias

Automation bias occurs when human investigators give excessive weight to an automated result.

Example:

Algorithm = 95% suspicious.

The compliance officer may unconsciously interpret every subsequent fact as supporting the algorithm.

This can produce:

algorithm → investigator → confirmation bias → regulatory report.

The proper approach should instead be:

algorithm → hypothesis → independent investigation → evidence → legal conclusion.

21. Trade-Secret Problem

Many sophisticated market-surveillance algorithms are proprietary.

The institution may argue:

“Our detection model is commercially confidential.”

But the affected person may respond:

“How can I challenge the allegation if I cannot understand how the system classified my trade?”

This creates a conflict between:

confidentiality;

intellectual property;

regulatory secrecy;

defence rights;

procedural fairness.

Possible solutions include:

independent expert inspection;

confidential judicial review;

disclosure to the regulator;

controlled disclosure;

technical expert reports;

protected access to relevant methodology.

22. Privacy and Communications Data

Modern detection systems may analyse:

telephone metadata;

email metadata;

messaging records;

trading chats;

employee communications;

contact networks.

Joined Cases VD and SR demonstrate that the importance of such information for insider-trading investigations does not permit unrestricted general retention of communications data. (EUR-Lex)

Therefore:

The usefulness of data for algorithmic detection does not automatically make its collection lawful.

23. Evidentiary Chain

A court considering an algorithmic detection claim should examine:

Stage 1 — Collection

Was the data lawfully collected?

Stage 2 — Preservation

Was the original data preserved?

Stage 3 — Processing

Was the data accurately processed?

Stage 4 — Algorithm

Was the model appropriate and validated?

Stage 5 — Alert

What exactly did the algorithm identify?

Stage 6 — Human investigation

Was there independent review?

Stage 7 — Regulatory report

What information was actually reported?

Stage 8 — Legal decision

What evidence ultimately supported the allegation?

An error at one stage does not necessarily invalidate everything that follows, but it may affect reliability, admissibility, weight, or liability depending upon national law.

24. Civil-Law Liability of Algorithm Providers

Suppose a bank purchases a market-surveillance system.

The contract promises:

“The system will detect suspicious insider-trading patterns with specified performance characteristics.”

The software repeatedly generates materially defective results.

Potential contractual questions include:

Was the system defective?

Did it satisfy contractual specifications?

Were limitations disclosed?

Was validation promised?

Was the client trained properly?

Was the system updated?

Were warnings ignored?

A contractual claim may therefore arise independently of the underlying insider-trading allegation.

25. Professional Negligence

A bank or broker may face a negligence-type claim under national law if it:

implemented an unsuitable system;

ignored known limitations;

failed to validate the system;

relied blindly on alerts;

failed to investigate obvious false positives;

failed to maintain the system.

The precise cause of action and standard of care depend upon the relevant national civil law.

26. Regulatory Liability

An institution may also face regulatory consequences if its detection system does not meet applicable requirements.

The MAR framework requires appropriate arrangements, systems and procedures for detecting and reporting suspicious orders and transactions. ESMA has stated that this obligation applies broadly to persons professionally arranging or executing transactions. (ESMA)

Thus, both sides of the problem matter:

Over-detection

Too many innocent persons are investigated.

Under-detection

Actual market abuse escapes detection.

27. Causation

A claimant alleging algorithmic detection error must usually confront causation.

Example:

defective algorithm → false alert → investigation → trading suspension → loss.

But another possibility is:

defective algorithm → false alert → independent investigation → independent evidence establishes violation.

In the second scenario, proving that the algorithm caused the ultimate loss may be more difficult.

Therefore, courts may examine the entire causal chain.

28. Damages

Depending on national law and the circumstances, possible damages could include:

Economic loss

lost trading opportunities;

lost employment income;

legal costs;

business losses.

Reputational loss

Particularly significant for financial professionals.

Contractual loss

Loss caused by termination or suspension.

Data-protection damages

Where unlawful processing of personal data causes compensable harm.

Regulatory-related loss

Where an unlawful or defective investigation causes measurable damage.

The availability and calculation of damages are primarily determined by the applicable national legal regime.

29. False-Positive Claim: Hypothetical

Assume an investment bank uses an AI surveillance platform.

The model detects:

“Employee A traded 48 hours before a company announcement.”

The algorithm assigns:

99% insider-trading risk.

The bank reports the employee.

Investigation later establishes:

the employee had no access to the information;

the trade was made pursuant to a pre-existing investment plan;

the timing was coincidental;

the algorithm ignored the investment plan.

The employee may argue:

the model was incorrectly designed;

relevant information was ignored;

the alert was improperly interpreted;

human review was inadequate;

the reporting decision caused financial/reputational harm.

The key issue is not simply:

“Was the algorithm wrong?”

It is:

Was the use and interpretation of the algorithm legally unreasonable and causally connected to the claimed harm?

30. False-Negative Claim: Hypothetical

Now reverse the situation.

An insider trades immediately before a major announcement.

The algorithm fails to flag the transaction.

Later investigation finds:

the person had access to confidential information;

communications preceded the trade;

the trade generated substantial profit.

Potential questions include:

Was the model appropriately configured?

Was the relevant data included?

Was the model properly validated?

Were warnings ignored?

Was there inadequate human supervision?

Did the institution breach regulatory obligations?

31. Standard of Proof

An important distinction must be made between:

Algorithmic probability

and

legal standard of proof.

A model may output:

90% likelihood of suspicious behaviour.

That number does not automatically become the legal standard of proof applicable to the proceedings.

The court or regulator must apply the legally relevant standard under the applicable national and EU framework.

32. Evidence Obtained Through Unlawful Data Collection

Joined Cases VD and SR are particularly important.

The CJEU held that general and indiscriminate retention of traffic data for market-abuse investigations was incompatible with EU law, while the treatment of evidence obtained contrary to EU law is generally governed by national procedural law subject to EU principles of equivalence and effectiveness. (EUR-Lex)

Therefore:

Unlawful data collection + accurate algorithm ≠ automatically lawful evidence.

The legality of the data acquisition must be considered separately.

33. Algorithmic Detection and Right to Silence

Under Consob, financial-market investigations do not eliminate fundamental rights merely because the regulator is investigating serious market abuse.

The CJEU recognised protection against compelled self-incrimination in proceedings involving sanctions of a criminal nature. (curia)

Therefore, an algorithmic suspicion cannot justify:

“Because the system identified you as suspicious, you must explain everything.”

Procedural safeguards remain applicable.

34. Insider Lists and Algorithmic Errors

Insider lists are important inputs for surveillance systems.

Potential errors include:

person incorrectly added;

person omitted;

incorrect time of inclusion;

incorrect time of removal;

inaccurate access records.

The 2026 Finansinspektionen v Carnegie Investment Bank judgment demonstrates the continuing importance of the legal significance of insider-list information. (Curia)

A detection algorithm using an incorrect insider list may therefore produce a structurally defective result.

35. Market Rumours

Algorithms increasingly scan:

news;

social media;

analyst reports;

financial blogs;

online forums.

The Autorité des marchés financiers, C-302/20 judgment demonstrates that information concerning forthcoming publication of a press article and market rumours can raise complex insider-information questions. (curia)

Thus:

“Online mention + trading + price movement”

should not automatically become:

“insider trading.”

The content, precision and circumstances must be examined.

36. Algorithmic Detection and Proportionality

A detection system should be proportionate to its purpose.

An extremely aggressive system may:

generate huge numbers of false positives;

unnecessarily investigate employees;

collect excessive personal data;

create excessive compliance costs.

An excessively weak system may:

miss genuine insider dealing;

expose investors to harm;

fail regulatory requirements.

European law therefore increasingly involves a balance between:

effective market surveillance

and

privacy, procedural fairness and individual rights.

37. Important Case-Law Table

38. Five Main Legal Questions in an Error Claim

A European court could effectively have to consider five separate questions.

Question 1 — Was the algorithm technically defective?

This is a technology/expert-evidence issue.

Question 2 — Was the underlying data accurate and lawfully obtained?

This is a data-protection and evidence issue.

Question 3 — Was the algorithmic output correctly interpreted?

This is a factual and legal assessment.

Question 4 — Did humans independently review the output?

This concerns procedural safeguards.

Question 5 — Did the error cause legally compensable damage?

This is the civil-liability question.

These questions should not be collapsed into one issue.

39. Algorithmic Error Versus Human Error

It is important not to assume:

algorithm error = liability.

A human compliance officer can also make an error.

For example:

Algorithm correctly identifies unusual activity

↓

Compliance officer incorrectly concludes insider dealing

Here the central problem may be human interpretation, not defective software.

Conversely:

Algorithm incorrectly identifies normal activity

↓

Compliance officer reasonably relies on the system

Here the algorithm/provider/institution's responsibilities may become central.

40. Role of Expert Evidence

In a civil claim, an expert may examine:

model architecture;

training data;

statistical methodology;

performance metrics;

false-positive rate;

false-negative rate;

system logs;

model version;

thresholds;

validation documents;

audit reports.

The court should distinguish:

technical reliability

from

legal sufficiency of the evidence.

An expert can explain why a model is statistically unreliable, but the ultimate legal assessment remains for the court.

41. Model Drift

A particularly modern problem is model drift.

Suppose:

Model trained in 2022.

Market conditions change dramatically in 2025.

The model continues operating.

Its historical accuracy may no longer represent current performance.

Possible causes include:

new trading strategies;

new financial instruments;

new market structures;

new automated trading techniques;

changed volatility;

changed investor behaviour.

A civil claim may therefore examine whether the institution had appropriate processes for detecting model degradation.

42. High-Frequency Trading

Algorithmic surveillance becomes particularly difficult with high-frequency trading because millions of:

orders;

cancellations;

modifications;

executions

may occur.

ESMA has specific regulatory material concerning algorithmic and high-frequency trading, including mechanisms designed to facilitate identification of order flow and detection of market abuse. (ESMA)

This makes manual review impossible for every transaction.

But automation creates a corresponding requirement:

The detection system itself must be appropriately designed and supervised.

43. Civil-Law Classification of the Claim

An algorithmic insider-trading detection dispute may potentially involve:

Contract

Between:

bank and software provider;

broker and technology vendor;

financial institution and employee.

Tort/delict

For negligent or unlawful conduct causing damage.

Data protection

For unlawful processing or disclosure of personal data.

Employment law

Where an employee is wrongly accused or dismissed.

Financial regulation

Where MAR obligations are involved.

Procedural law

Where algorithmic evidence is challenged in litigation.

Therefore, “algorithmic insider-trading error” is better understood as a cross-disciplinary civil-liability problem.

44. Practical Judicial Framework

A European court confronted with such a dispute could ask:

Step 1

What exactly did the algorithm detect?

Step 2

What legal proposition is the claimant trying to prove with that output?

Step 3

What data were supplied to the system?

Step 4

Were those data accurate and lawfully obtained?

Step 5

Was the model appropriately validated?

Step 6

What was its error rate?

Step 7

Was the output independently reviewed?

Step 8

Were alternative explanations considered?

Step 9

Could the affected person meaningfully challenge the result?

Step 10

Was the final decision based solely or substantially on the algorithm?

Step 11

Was the decision consistent with MAR?

Step 12

Did the alleged error cause legally compensable damage?

45. Key Principles

The European case law supports the following principles:

Algorithmic detection is not equivalent to proof.

Inside information must satisfy the legal definition under MAR.

Statistical abnormality does not automatically establish insider dealing.

Context matters.

False positives can have legal consequences.

False negatives can create regulatory and potentially civil consequences.

The legality of underlying data collection matters.

General and indiscriminate communications-data retention is subject to strict EU-law limits.

Fundamental rights remain applicable during market-abuse investigations.

Human review remains important.

Trade secrets do not automatically eliminate the possibility of meaningful legal scrutiny.

Technical probability does not automatically satisfy a legal standard of proof.

Causation must be established for a damages claim.

National civil and procedural law remains important because EU law does not create a complete European code of civil liability for algorithmic detection errors.

46. Short Exam Answer

Algorithmic insider-trading detection error claims in Europe arise when AI or automated market-surveillance systems incorrectly identify or fail to identify suspected insider dealing. The principal framework is the EU Market Abuse Regulation, supplemented by data-protection, privacy, fundamental-rights and national civil-law principles. In Spector Photo Group (C-45/08), the CJEU emphasised the purpose of the insider-dealing prohibition; Geltl (C-19/11) and Lafonta (C-628/13) explain the concept of precise inside information; Autorité des marchés financiers (C-302/20) demonstrates the importance of contextual assessment of market rumours and disclosures; Consob (C-481/19) protects the right to silence in market-abuse proceedings; VD and SR (C-339/20 and C-397/20) restrict indiscriminate retention of communications data used for insider-trading investigations; Georgakis (C-391/04) addresses coordinated transactions; and Finansinspektionen v Carnegie (C-363/24) provides a recent interpretation of precise inside information and insider-list communications. The central principle is that an algorithmic alert is an investigative indicator rather than an automatic legal finding of insider dealing. A court examining an error claim should consider data quality, model reliability, false-positive/false-negative rates, lawful acquisition of evidence, human review, procedural safeguards, causation and actual damage. (Infocuria)

Ultra-Short Revision

Algorithmic Insider Trading Detection =

Trading data → AI detection → suspicious alert → human investigation → regulatory action

Main risks:

False positive + false negative + bad data + model error + automation bias + unlawful surveillance data + lack of human review

Key cases:

Spector Photo Group — C-45/08

Georgakis — C-391/04

Geltl — C-19/11

Lafonta — C-628/13

AMF — C-302/20

Consob — C-481/19

VD & SR — C-339/20 & C-397/20

Finansinspektionen v Carnegie — C-363/24

Core rule:

An algorithm may detect suspicious behaviour, but the legal conclusion of insider dealing requires independent application of the MAR legal criteria and appropriate procedural safeguards.