ChatGLM-based Financial Data Analysis System: Solving the Challenges of Large Model Implementation in the Financial Sector

This article introduces a large language model application system for the financial sector. By automating PDF financial report processing and optimizing data extraction workflows, it addresses the performance limitations of general-purpose large models in financial professional tasks, providing a practical technical solution for financial data analysis. Built on the domestic ChatGLM large model, the system integrates domain fine-tuning, knowledge enhancement, and other technologies, covering multiple scenarios such as investment research, credit review, and regulatory technology, helping to improve the efficiency and accuracy of financial data analysis.

Financial data analysis is a core task in investment research, risk control, and compliance. However, traditional methods face efficiency bottlenecks: chaotic PDF formats (diverse document formats from different institutions/periods make traditional tools ineffective), low data processing efficiency (analysts manually organize data which is tedious and error-prone), and limitations of general-purpose large models (misinterpreting financial terms and generating hallucinations, affecting decision-making). These pain points led to the development of this system.

The system is built on ChatGLM (Chinese-optimized, open-source customizable, flexible deployment, cost-controllable) to form an end-to-end pipeline: 1. Intelligent document parsing (layout analysis, table recognition and reconstruction, OCR/text extraction, semantic chunking); 2. Financial knowledge enhancement (financial indicator knowledge base, industry classification, regulatory rule embedding); 3. Intelligent Q&A and summary generation (indicator extraction, trend comparison, risk early warning, interactive Q&A). Technical highlights include domain fine-tuning (continued pre-training + instruction fine-tuning + RAG), multimodal fusion (chart understanding, image-text correlation), and credibility assessment (confidence scoring, cross-validation, traceability display).

The system delivers value across multiple scenarios: improved investment research efficiency (reducing manual time to a few minutes), credit risk review (automated financial report analysis to identify risks), regulatory technology applications (monitoring the quality of information disclosure), and corporate financial management (building internal financial knowledge bases).

The current system has limitations: the ability to handle complex tables needs improvement, multi-language document support is limited, real-time performance requires balancing speed and accuracy, and model hallucinations still need manual review. Future improvement directions include introducing more powerful multimodal models, improving financial knowledge graphs, and developing intelligent interactive interfaces.

This project demonstrates the implementation path of large models in professional fields: deep dive into domain pain points → build end-to-end pipelines → domain fine-tuning and knowledge enhancement → credibility assessment. For AI applications in professional fields, the strategy of "general base + domain adaptation + engineering optimization" is worth learning. The technical value lies in solving practical problems with controllable costs and risks. In the future, AI will help financial analysts free their hands and focus on insight and decision-making.