Leveraging Internet-Sourced Text Data for Financial Analytics in Supply Chain Finance: A Large Language Model-Enhanced Text Mining Workflow
提出一种无需编程技能的大语言模型增强文本挖掘工作流，利用互联网招标网站和财务报表数据，高效分析新能源客车供应链金融业务，实验证明该方法比传统方法更高效实用。
In the era of artificial intelligence and fintech, improving the efficiency of financial analysis is essential for financial service providers. This article proposes a novel large language model-enhanced text mining workflow that leverages Internet-sourced text information to efficiently analyze supply chain finance business without requiring programming skills. We conduct a case study on the Chinese market for new energy buses—a rapidly growing sector due to government incentives and the push for sustainable urban transportation—using data from bidding websites and financial statements. The experimental results demonstrate that our LLM-enhanced workflow outperforms traditional methods, showcasing increased efficiency and practicality, especially for non-programming employees in supply chain financial services.