Agentic GraphRAG: Navigating Unstructured Financial Data with Collaborative AI
The article introduces the Agentic GraphRAG framework designed for analyzing unstructured financial data. This system enhances the usability of public registries by combining structured records with unstructured legal text. The framework has shown significant improvements in various performance metrics compared to traditional methods.
- ▪The Agentic GraphRAG framework facilitates expert analysis of commercial registry data.
- ▪It employs a three-phase pipeline to build a Neo4j knowledge graph from structured and unstructured data.
- ▪The system outperforms standard agentic vector-RAG baselines in correctness, relevance, and information recall.
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Computer Science > Information Retrieval arXiv:2605.18770 (cs) [Submitted on 15 Apr 2026] Title:Agentic GraphRAG: Navigating Unstructured Financial Data with Collaborative AI Authors:Arthur Capozzi, Dirk Helbing View a PDF of the paper titled Agentic GraphRAG: Navigating Unstructured Financial Data with Collaborative AI, by Arthur Capozzi and 1 other authors View PDF HTML (experimental) Abstract:We present a collaborative agentic GraphRAG framework for expert analysis of commercial registry data. Public registries are often formally accessible, yet difficult to use in practice because they combine structured records with large volumes of unstructured legal text.
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