Evidence-Supported Credit Risk Report Generation Using News-Centric Financial Knowledge Graphs
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 c
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorRocio Jimenez-Villen →
Evidence-Supported Credit Risk Report Generation Using News-Centric Financial Knowledge Graphs
- Linked via arxiv authorZiwei Xu →
Evidence-Supported Credit Risk Report Generation Using News-Centric Financial Knowledge Graphs
- Linked via arxiv authorYing Chen →
Evidence-Supported Credit Risk Report Generation Using News-Centric Financial Knowledge Graphs
- Linked via arxiv authorOscar Araque →
Evidence-Supported Credit Risk Report Generation Using News-Centric Financial Knowledge Graphs
- Linked via arxiv authorRyutaro Ichise →
Evidence-Supported Credit Risk Report Generation Using News-Centric Financial Knowledge Graphs