FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models

Paper: arXiv 2405.14767

Abstract

As financial institutions and professionals increasingly incorporate Large Language Models (LLMs) into their workflows, substantial barriers, including proprietary data and specialized knowledge, persist between the finance sector and the AI community. These challenges impede the AI community’s ability to enhance financial tasks effectively. Acknowledging financial analysis’s critical role, we aim to devise financial-specialized LLM-based toolchains and democratize access to them through open-source initiatives, promoting wider AI adoption in financial decision-making. In this paper, we introduce FinRobot, a novel open-source AI agent platform supporting multiple financially specialized AI agents, each powered by LLM. Specifically, the platform consists of four major layers: 1) the Financial AI Agents layer that formulates Financial Chain-of-Thought (CoT) by breaking sophisticated financial problems down into logical sequences; 2) the Financial LLM Algorithms layer dynamically configures appropriate model application strategies for specific tasks; 3) the LLMOps and DataOps layer produces accurate models by applying training/fine-tuning techniques and using task-relevant data; 4) the Multi-source LLM Foundation Models layer that integrates various LLMs and enables the above layers to access them directly. Finally, FinRobot provides hands-on for both professional-grade analysts and laypersons to utilize powerful AI techniques for advanced financial analysis. We open-source FinRobot at \url{“https://github.com/AI4Finance-Foundation/FinRobot"}.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper introduces a modular platform architecture with operational layers rather than heavy mathematical derivations, resulting in moderate complexity. It demonstrates practical implementation with a public GitHub repository and specific evaluation cases, giving it solid empirical grounding for application development.

Research Flowchart

  flowchart TD
  A["Research Goal: Develop Financial-Specialized LLM Platform"] --> B["Key Methodology: Four-Layer Architecture"]
  
  subgraph B ["Four-Layer Architecture"]
      B1["Financial AI Agents<br/>Financial CoT Formulation"]
      B2["Financial LLM Algorithms<br/>Dynamic Model Configuration"]
      B3["LLMOps & DataOps<br/>Model Training/Fine-tuning"]
      B4["Multi-source LLM Foundation<br/>Integrated LLM Access"]
  end
  
  B --> C["Computational Process: Pipeline Execution"]
  C --> D["Key Outcomes: FinRobot Platform"]
  
  D --> D1["Open-Source AI Agent Platform<br/>github.com/AI4Finance-Foundation/FinRobot"]
  D --> D2["Specialized Financial Analysis<br/>For Professional & Layperson Users"]
  D --> D3["Democratized Access to Financial AI Tools"]

Cited References (85)

1. Language models are few-shot learners (2020)
2. BloombergGPT : A large language model for finance (2023) — link
3. FinRL-Meta : Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning (2022)
4. Dynamic Datasets and Market Environments for Financial Reinforcement Learning (2024)
5. FinRL : Deep reinforcement learning framework to automate trading in quantitative finance (2021)
6. FinRL : A deep reinforcement learning library for automated stock trading in quantitative finance (2020)
7. A Practical Machine Learning Approach for Dynamic Stock Recommendation — → 2511.12129
8. LoRA : Low-rank adaptation of large language models (2021)

6 of 85 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.