In a world where large language models like ChatGPT make information more accessible than ever, a critical question remains: how do we move from generated answers to reliable, structured intelligence—especially in high-stakes domains like finance?

This talk explores the gap between LLM-based exploration and data-driven decision-making, and introduces an alternative approach based on structured, real-time news data.

We will show how NewsAPI.ai, built on top of the Event Registry platform, processes over 150,000 global sources to transform unstructured articles into enriched, machine-readable signals—including entities, sentiment, categories, and event detection.

Key topics include:

The limitations of LLM-only approaches in financial and academic research How structured news data enables trend detection, risk monitoring, and signal extraction Event-based clustering and metadata enrichment as a foundation for analysis A brief comparison with GDELT, highlighting differences in data structure, usability, and analytical depth The role of MCP (Model Context Protocol) in connecting LLMs with structured news systems, enabling workflows such as scan → triage → retrieve

The session will demonstrate how combining LLMs with structured news infrastructure creates a more reliable and scalable approach to financial intelligence, research, and innovation.