# Structuring the Unstructured: A Multi-Agent System for Extracting and Querying Financial KPIs and Guidance

arXiv: https://arxiv.org/abs/2505.19197 · HTML: https://mlsanity.com/paper/2505.19197/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 25 May 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 6 of 6)

- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2504.00587/) (2025)
- [Enhancing Text-to-SQL Translation for Financial System Design](https://mlsanity.com/paper/2312.14725/) (2023)
- [BookSQL: A Large Scale Text-to-SQL Dataset for Accounting Domain](https://mlsanity.com/paper/2406.07860/) (2024)
- [Multi-Document Financial Question Answering using LLMs](https://mlsanity.com/paper/2411.07264/) (2024)
- [Evaluating Large Language Models on Financial Report Summarization: An Empirical Study](https://mlsanity.com/paper/2411.06852/) (2024)

## Built on by (most cited first, 2 of 2)

- [MedPAO: A Protocol-Driven Agent for Structuring Medical Reports](https://mlsanity.com/paper/2510.04623/) (2025)
- [Toward Trustworthy Evaluation of Sustainability Rating Methodologies: A Human-AI Collaborative Framework for Benchmark Dataset Construction](https://mlsanity.com/paper/2602.17106/) (2026)

## Abstract (arXiv)

Extracting structured and quantitative insights from unstructured financial filings is essential in investment research, yet remains time-consuming and resource-intensive. Conventional approaches in practice rely heavily on labor-intensive manual processes, limiting scalability and delaying the research workflow. In this paper, we propose an efficient and scalable method for accurately extracting quantitative insights from unstructured financial documents, leveraging a multi-agent system composed of large language models. Our proposed multi-agent system consists of two specialized agents: the Extraction Agent and the Text-to-SQL Agent. The textitExtraction Agent automatically identifies key performance indicators from unstructured financial text, standardizes their formats, and verifies their accuracy. On the other hand, the textitText-to-SQL Agent generates executable SQL statements from natural language queries, allowing users to access structured data accurately without requiring familiarity with the database schema. Through experiments, we demonstrate that our proposed system effectively transforms unstructured text into structured data accurately and enables precise retrieval of key information. First, we demonstrate that our system achieves approximately 95% accuracy in transforming financial filings into structured data, matching the performance level typically attained by human annotators. Second, in a human evaluation of the retrieval task -- where natural language queries are used to search information from structured data -- 91% of the responses were rated as correct by human evaluators. In both evaluations, our system generalizes well across financial document types, consistently delivering reliable performance.