{
  "id": 10683,
  "url": "https://link.springer.com/article/10.1007/s10462-026-11645-z",
  "title": "Towards transparent financial AI: a systematic review of graph learning and explainable methods for credit risk and fraud detection",
  "summary": "Graph-based learning and explainable artificial intelligence (XAI) are increasingly used to improve both predictive performance and transparency in financial risk modelling. This paper presents a systematic literature review of AI and machine learning approaches for credit risk assessment and fraud detection, with specific attention to graph-based methods and explainable frameworks. Following a PRISMA-guided methodology, 149 studies published between 2015 and 2025 were analysed across multiple a",
  "authors": null,
  "category": "research",
  "topics": "transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-15T00:00:00.000Z",
  "fetched_at": "2026-07-16T05:10:56.605Z",
  "source_slug": "x-artificial-intelligence-review",
  "source_name": "Artificial Intelligence Review",
  "source_homepage": "https://link.springer.com/journal/10462",
  "ethics_ai_record_url": "https://ethics.ai/record/10683",
  "original_url": "https://link.springer.com/article/10.1007/s10462-026-11645-z",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}