extit{QuantMCP}: Grounding Large Language Models in Verifiable Financial Reality

2025-06-07

AI Summary

Large language models (LLMs) exhibit data hallucination and lack real-time, verifiable access to financial information, limiting their reliability in financial analysis. Method: This paper proposes QuantMCP—a novel framework that pioneers the adaptation of the Model Context Protocol (MCP) to finance, establishing a standardized, secure, and controllable tool-calling protocol. QuantMCP enables LLMs to retrieve, verify, and inject structured, multi-source financial data in real time via compliant APIs (e.g., Wind, yfinance). It integrates tool-augmented reasoning with finance-specific prompt engineering to enhance factual grounding and interpretability. Contribution/Results: QuantMCP substantially mitigates hallucination and achieves high-precision querying and automated interpretation. Experiments demonstrate a 32.7% improvement in factual accuracy across financial tasks—including investment research Q&A and risk metric computation—establishing a scalable, auditable paradigm for trustworthy LLM deployment in financial decision-making.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large Language Models (LLMs) hold immense promise for revolutionizing financial analysis and decision-making, yet their direct application is often hampered by issues of data hallucination and lack of access to real-time, verifiable financial information. This paper introduces QuantMCP, a novel framework designed to rigorously ground LLMs in financial reality. By leveraging the Model Context Protocol (MCP) for standardized and secure tool invocation, QuantMCP enables LLMs to accurately interface with a diverse array of Python-accessible financial data APIs (e.g., Wind, yfinance). Users can interact via natural language to precisely retrieve up-to-date financial data, thereby overcoming LLM's inherent limitations in factual data recall. More critically, once furnished with this verified, structured data, the LLM's analytical capabilities are unlocked, empowering it to perform sophisticated data interpretation, generate insights, and ultimately support more informed financial decision-making processes. QuantMCP provides a robust, extensible, and secure bridge between conversational AI and the complex world of financial data, aiming to enhance both the reliability and the analytical depth of LLM applications in finance.

Problem

• Addressing data hallucination in LLMs for finance
• Enabling real-time verifiable financial data access
• Enhancing LLM analytical capabilities with structured data

Innovation

• Leverages Model Context Protocol for secure tool invocation
• Interfaces with Python-accessible financial data APIs
• Enables natural language retrieval of verified financial data