Agentic AI
in Markets
Original analysis, research commentary, and practitioner perspectives on the AI transformation of financial markets — from the team behind RiskAICenter and BigDataFinance.org.
Agentic AI Is Rewriting Risk Management. Here's What That Means for Your Desk.
Autonomous agents don't just monitor risk — they act on it. The shift from passive dashboards to AI systems that adjust positions, flag limit breaches, and draft regulatory filings in real time is already underway at tier-one banks. This is what you need to understand before your competitors do.
Why LLM Hallucination Is a Systemic Risk Problem, Not Just a Tech Problem
When a large language model confidently cites a regulation that doesn't exist, the problem isn't just a bad output — it's a governance failure waiting to happen. Understanding hallucination at a mechanistic level is now a core competency for anyone deploying AI in a regulated financial institution.
Gore Mountain & the Agentic AI Ski Summit: Why This Conference Format Works
BigDataFinance.org ran 15 years of conferences that consistently attracted 300+ of the most senior quantitative finance professionals in the world. What made them work wasn't the content alone — it was the setting. Here's why Gore Mountain and the alpine format produce better research conversations than any hotel ballroom in Midtown.
Multi-Agent Portfolio Construction: From Theory to Live Deployment
The idea of using multiple coordinating AI agents for portfolio construction has moved from academic papers to live production deployments at systematic hedge funds. This is what the architecture actually looks like — and what it requires to work reliably.
The AI Regulatory Landscape for Financial Firms: What's Actually Changing in 2026
The EU AI Act came into force. The SEC issued updated guidance on AI in investment advice. The Basel Committee published a discussion paper on AI in credit risk models. 2026 is not a year to wait for regulatory clarity — it's a year to act on the clarity that already exists.
Building LLM Pipelines Over Alternative Data: What Actually Works
Earnings call transcripts, SEC filings, satellite imagery metadata, social media sentiment — alternative data sources promise alpha. LLMs promise to extract it at scale. The reality is more nuanced, and the firms generating real edge are solving problems that most teams don't even know exist yet.
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