Evidence-Supported Credit Risk Report Generation Using News-Centric Financial Knowledge Graphs
arXiv:2607.01023v1 Announce Type: new Abstract: 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
The proliferation of financial news and the advancement of NLP techniques make it increasingly feasible to extract and model complex relationships within financial data at scale.
This development allows for more transparent and evidence-based credit risk assessment, potentially reducing financial instability and improving market efficiency by explicitly linking real-world events to financial outcomes.
Credit risk reporting can now be systematically generated with explicit, news-backed evidence, moving beyond subjective interpretations to a more data-driven and verifiable process.
- · Financial institutions (risk management)
- · Credit rating agencies
- · Quantitative analysts
- · AI/ML developers in finance
- · Traditional, manual credit assessment methods
- · Companies with opaque financial reporting
- · Fraudulent entities (increased detection)
Improved accuracy and transparency in credit risk assessments due to explicit linkage of financial data with real-world news events.
Enhanced market efficiency and reduced systemic risk as credit decisions become more robust and less susceptible to implicit biases.
The development of real-time, autonomous financial markets where credit and investment decisions are continuously updated based on live news feeds integrated into knowledge graphs.
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Read at arXiv cs.CL