{
  "id": 1844087,
  "title": "Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models",
  "url": "https://urgent.news/2026/08/18/communicating-credit-risk-with-large-language-models-evaluation-of",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-18T12:39:46.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.17715v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Credit decisioning is a high-stakes task in which model outputs must be accurate and explainable to support compliant decisions. Although modern credit risk models such as eXtreme Gradient Boosting (XGBoost) and Graph Neural Networks (GNNs) improve predictive performance, their explanations are often too technical for stakeholders creating communication gaps that can shape approvals, denials, and…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}