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AI and Structured Data Forum: Optimizing Performance
This in-person event co-produced by the Center for Research toward Advancing Financial Technologies (CRAFT) and XBRL US, will explore how the use of structured, standardized data improves the ability of large language models to accurately, consistently understand financial and business information.
Point of View
Getting XBRL in LLMs for as-filed research
It’s generally accepted that structured data improves analysis and recent AI advances mean getting machines to find and access machine-understandable structured data is also getting better.
This post touches on four topics related to XBRL in research tasks supported by AI.
XBRL Data Community
Join the growing number of professionals using the XBRL application programming interface (API) to get reported data and check reports before filing.
Check out our growing collection of free resources, including:
AI Meets Auditing: Build Smarter Accounting Estimates with Multi-Agent LLMs
What if your accounting estimates could stress-test themselves? Join us to explore how multi-agent AI systems powered by Large Language Models could change how estimates are prepared and refine and improve the accuracy of accounting estimates. Agents can systematically test various modifiers through an iterative model dynamically simulating “what could go wrong” scenarios. Agents can […more]
California: Digital First Climate Data
To meet the goals of climate initiatives worldwide requires governments to understand the impact of industry on the environment. This can only be accomplished through standardized, digital data that is concretely understood, timely, and consistently prepared to foster a shared understanding of the current state of climate risk and to monitor changes going forward.
