用新闻构建金融知识图谱，提升信用报告生成质量与可信度
Evidence-Supported Credit Risk Report Generation Using News-Centric Financial Knowledge Graphs
- 以新闻事件为锚点构建公司中心的知识图谱
- 在三大金融维度上生成报告，质量提升19%-34%，幻觉减少
- 适合金融风控、智能投研人员参考
金融市场随新闻报道的现实事件动态演变，但这些驱动因素常隐含于文本中。为更清晰解释市场动态，需通过事实性、公司为中心且环境感知的知识图谱显式建模事件与市场关系。我们提出FinKG-News框架，自动提取新闻事件作为锚点，关联公司实体构建知识图谱。基于此图谱提供的结构化证据，融合事件、新闻与公司数据，开发了一种上下文学习架构，用于跨三大核心金融维度的信用风险报告生成。自动与人工评估均表明，当前幻觉检测与质量评估仍不可靠，专家判断不可或缺。我们的方法持续优于基线，报告质量提升19%-34%，同时减少幻觉。源代码与项目资源已公开：https://github.com/ichise-laboratory/FINKG-news。
原文摘要 · Abstract (English)
Financial markets evolve in response to real-world events reported in news, yet these drivers often remain implicit in text. To better explain market dynamics, event-market relations must be explicitly modeled through factual, company-centric, and environment-aware knowledge graphs. We present FinKG-News, a framework that automatically constructs such graphs by extracting news events as anchors linked to companies. Using FinKG-News as grounded evidence that integrates events, news, and company data, we develop an in-context learning architecture for credit risk report generation across three core financial dimensions. Automatic and human evaluations show that automated hallucination detection and quality assessment remain unreliable, making expert judgment indispensable. Our approach consistently outperforms baselines, improving quality by 19%-34% while reducing hallucinations. The source code and project resources are publicly available at: https://github.com/ichise-laboratory/FINKG-news.
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