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Evidence-Supported Credit Risk Report Generation Using News-Centric Financial Knowledge Graphs

Rocio Jimenez-Villen, Ziwei Xu, Ying Chen, Oscar Araque, Ryutaro Ichise · Jul 1, 2026 · Citations: 0

Human Eval
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How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

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 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.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.
  • The abstract does not clearly name benchmarks or metrics.
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Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 30%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Financial markets evolve in response to real-world events reported in news, yet these drivers often remain implicit in text."

Evaluation Modes

partial

Human Eval

Includes extracted eval setup.

"Financial markets evolve in response to real-world events reported in news, yet these drivers often remain implicit in text."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Financial markets evolve in response to real-world events reported in news, yet these drivers often remain implicit in text."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Financial markets evolve in response to real-world events reported in news, yet these drivers often remain implicit in text."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Financial markets evolve in response to real-world events reported in news, yet these drivers often remain implicit in text."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"Automatic and human evaluations show that automated hallucination detection and quality assessment remain unreliable, making expert judgment indispensable."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Domain Experts
  • Expertise required: Coding

Evaluation Details

  • Evaluation modes: Human Eval
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

Financial markets evolve in response to real-world events reported in news, yet these drivers often remain implicit in text.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • 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.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Human evaluation, Simulation environment) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Research Summary

Contribution Summary

  • 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.

Why It Matters For Eval

  • Automatic and human evaluations show that automated hallucination detection and quality assessment remain unreliable, making expert judgment indispensable.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Human Eval

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

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