Artificial intelligence in EU securities markets
This risk analysis report by the European Securities and Markets Authority provides an overview of artificial intelligence use cases across EU securities markets. It assesses the degree of AI adoption in asset management, trading, and other market areas such as credit rating agencies and proxy advisory firms. The report also highlights potential risks associated with AI uptake, including explainability issues, concentration, systemic risk, and algorithmic bias.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Regulators, financial market participants, and researchers seeking an overview of artificial intelligence adoption and risk in European securities markets.
What the document contains
The document covers the applications of artificial intelligence and machine learning in EU securities markets, specifically examining asset management, trade lifecycles (pre-trade, execution, and post-trade), credit rating agencies, proxy advisory firms, and associated operational and systemic risks.
The report does not provide legally binding rules, direct supervisory sanctions, or detailed definitions of AI beyond standard industry overviews.
- Artificial intelligence is increasingly adopted to support and optimize activities in EU securities markets, but does not lead to a fast and disruptive overhaul of business processes.
- In asset management, managers leverage AI for investment strategies, risk management, and compliance, though fully AI-based investment processes remain limited.
- AI models in trading help optimize trade execution and post-trade processes, reducing market impact and minimizing settlement failures.
- Credit rating agencies and proxy advisory firms mostly use AI tools to enhance information sourcing and data analysis rather than for core ratings or voting decisions.
- Key risks associated with AI uptake include lack of explainability, market concentration, systemic risk, algorithmic bias, and data quality issues.
· Summary · Introduction · Asset management · Trading · Other entities · Potential risks · Conclusion · References · Appendix: Analytical methods
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
3/5Clear responsibility for AI outcomes
Appropriate governance frameworks ensuring the accountability and responsibility of both AI providers and end users are warranted in order to mitigate risks stemming from the complexity of some AI systems
Conclusion
screening score · 2026-10-25
Fairness
3/5Non-discrimination and equitable treatment
Algorithmic bias is, in fact, an often -mentioned concern when AI influences financial decision - making. The term refers to a systematic behaviour of an algorithm creating outcomes that can be considered unfair
Potential risks
screening score · 2026-10-25
Human Dignity
0/5Respect for the inherent worth of persons
N/A
screening score · 2026-10-25
Privacy
2/5Protection of personal data and private life
Efforts are ongoing to pool data, although these initiatives are subject to data privacy concerns.
Trade execution
screening score · 2026-10-25
Enforcement
0/5Consequences and compliance mechanisms with real force
N/A
screening score · 2026-10-25
Impact Assessment
3/5Required assessment of an AI system's risks/impacts before or during use
In one of the major international efforts in this regard , the Commission presented its AI package in April 2021, including a proposal for a regulation laying down harmonised rules on AI (the ‘AI Act’) and a related impact assessment
Introduction
screening score · 2026-10-25