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FinSight: Towards Real-World Financial Deep Research

Jiajie Jin, Yuyao Zhang, Yimeng Xu, Hongjin Qian, Yutao Zhu, Zhicheng Dou

TL;DR

FinSight tackles the challenge of automatic, high-quality multimodal financial reporting by introducing a Code Agent with Variable Memory (CAVM) that unifies data, tools, and agents into a programmable memory space. It combines an Iterative Vision-Enhanced Mechanism to refine professional-grade visualizations with a Two-Stage Writing Framework that generates concise analytical chains before composing full reports with integrated visuals and citations. The framework orchestrates data collection, multi-turn analysis, and structured report generation via three core processes, enabling dynamic data gathering and deep, citation-aware analysis. Experimental results on a company- and industry-level benchmark show that FinSight significantly outperforms leading deep research systems in factual accuracy, analytical depth, and presentation quality, demonstrating a practical path toward human-expert-like automated financial reports.

Abstract

Generating professional financial reports is a labor-intensive and intellectually demanding process that current AI systems struggle to fully automate. To address this challenge, we introduce FinSight (Financial InSight), a novel multi agent framework for producing high-quality, multimodal financial reports. The foundation of FinSight is the Code Agent with Variable Memory (CAVM) architecture, which unifies external data, designed tools, and agents into a programmable variable space, enabling flexible data collection, analysis and report generation through executable code. To ensure professional-grade visualization, we propose an Iterative Vision-Enhanced Mechanism that progressively refines raw visual outputs into polished financial charts. Furthermore, a two stage Writing Framework expands concise Chain-of-Analysis segments into coherent, citation-aware, and multimodal reports, ensuring both analytical depth and structural consistency. Experiments on various company and industry-level tasks demonstrate that FinSight significantly outperforms all baselines, including leading deep research systems in terms of factual accuracy, analytical depth, and presentation quality, demonstrating a clear path toward generating reports that approach human-expert quality.

FinSight: Towards Real-World Financial Deep Research

TL;DR

FinSight tackles the challenge of automatic, high-quality multimodal financial reporting by introducing a Code Agent with Variable Memory (CAVM) that unifies data, tools, and agents into a programmable memory space. It combines an Iterative Vision-Enhanced Mechanism to refine professional-grade visualizations with a Two-Stage Writing Framework that generates concise analytical chains before composing full reports with integrated visuals and citations. The framework orchestrates data collection, multi-turn analysis, and structured report generation via three core processes, enabling dynamic data gathering and deep, citation-aware analysis. Experimental results on a company- and industry-level benchmark show that FinSight significantly outperforms leading deep research systems in factual accuracy, analytical depth, and presentation quality, demonstrating a practical path toward human-expert-like automated financial reports.

Abstract

Generating professional financial reports is a labor-intensive and intellectually demanding process that current AI systems struggle to fully automate. To address this challenge, we introduce FinSight (Financial InSight), a novel multi agent framework for producing high-quality, multimodal financial reports. The foundation of FinSight is the Code Agent with Variable Memory (CAVM) architecture, which unifies external data, designed tools, and agents into a programmable variable space, enabling flexible data collection, analysis and report generation through executable code. To ensure professional-grade visualization, we propose an Iterative Vision-Enhanced Mechanism that progressively refines raw visual outputs into polished financial charts. Furthermore, a two stage Writing Framework expands concise Chain-of-Analysis segments into coherent, citation-aware, and multimodal reports, ensuring both analytical depth and structural consistency. Experiments on various company and industry-level tasks demonstrate that FinSight significantly outperforms all baselines, including leading deep research systems in terms of factual accuracy, analytical depth, and presentation quality, demonstrating a clear path toward generating reports that approach human-expert quality.
Paper Structure (49 sections, 8 equations, 9 figures, 6 tables)

This paper contains 49 sections, 8 equations, 9 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Problem Formulation
  4. The Framework of FinSight
  5. Data Collection
  6. Data Analysis
  7. Report Generation
  8. Code Agent with Variable Memory (CAVM)
  9. Motivation
  10. Unified Variable Space
  11. Foundation Agent with Code Action
  12. Iterative Vision-Enhanced Mechanism for Visualization
  13. Two-Stage Writing with Generative Retrieval
  14. Experiments
  15. Dataset and Evaluation Metrics
  16. ...and 34 more sections

Figures (9)

  • Figure 1: Overview of the FinSight Framework.
  • Figure 2: The design philosophy of CAVM architecture.
  • Figure 3: Chain-of-Analysis Illustration.
  • Figure 4: Correlation between report length and quality score across different methods.
  • Figure 5: An example of our Iterative Vision-Enhanced Mechanism of Visualization. The chart is generated by matplotlib and seaborn package in Python.
  • ...and 4 more figures