Luca BertonAI in Asset Management: Where DORA Meets MiFID II
Asset managers increasingly depend on AI for alpha generation, risk management, and client reporting. From quantitative trading algorithms to ESG scoring models to natural language processing of earnings calls — AI infrastructure is now business-critical in asset management.
DORA creates new operational resilience requirements that overlap with existing MiFID II obligations, creating a complex compliance landscape for asset management AI.
Asset Management AI Use Cases Under DORA
Algorithmic Trading Systems
- DORA Art. 24-27: Trading algorithms must undergo resilience testing including market stress scenarios, exchange connectivity failures, and data feed outages
- MiFID II Art. 17: Algorithmic trading systems already need annual self-assessment — DORA adds operational resilience testing requirements
- Kill switches: Infrastructure must support immediate algorithm shutdown with audit trail
- Latency monitoring: Degraded AI inference speed during market volatility must trigger alerts
Portfolio Construction AI
- Model governance: AI-driven portfolio optimisation models need version control, approval workflows, and backtesting requirements
- Data dependency: Alternative data feeds (satellite, social, IoT) create third-party risks under DORA Art. 28-44
- Explainability: Investment committees need to understand AI recommendations — infrastructure must provide model interpretation
- Bias monitoring: Ensure AI doesn't create unintended sector/geography concentration risks
Risk Analytics & Reporting
- VaR and stress testing: AI-enhanced risk models must produce consistent results under infrastructure disruption
- Client reporting: Automated report generation using AI must have continuity planning — clients expect reports regardless of AI system status
- Regulatory reporting: UCITS KIID and PRIIPs KID generation using AI needs documented fallback procedures
DORA + MiFID II Compliance Matrix for AI
Overlapping Requirements
| Requirement | MiFID II | DORA |
|---|---|---|
| Business continuity | Art. 16 — organisational requirements | Art. 11 — response and recovery |
| Algo trading resilience | Art. 17 — algorithmic trading | Art. 24-27 — TLPT |
| Outsourcing | Art. 16(5) — critical functions | Art. 28-44 — ICT third parties |
| Incident management | RTS on reporting | Art. 17-23 — ICT incidents |
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- Low-latency inference: Trading AI needs sub-millisecond inference — infrastructure must maintain performance SLAs during disruption
- Multi-region deployment: UCITS depositaries across jurisdictions require data locality controls
- Audit logging: Every AI-influenced trading decision must be logged with model version, input features, and confidence scores
- Market data resilience: Multiple feed handlers with automated failover for AI that depends on real-time market data
DORA
asset management
trading
AI infrastructure
compliance
MiFID II
portfolio analytics
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