{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/ai-for-fraud-detection",
  "headline": "AI for Fraud Detection: Graph Neural Networks, Anti-Money Laundering, and Financial Crime",
  "description": "Financial fraud costs the global economy an estimated $5 trillion annually. AI — particularly graph neural networks — is transforming fraud detection from reactive rules to proactive pattern discovery, catching complex money laundering networks and transaction fraud that rule-based systems miss while reducing false alarms that waste investigator time.",
  "dateCreated": "2026-05-24T02:49:13.515Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "Reinforcement learning with graph neural network (RL-GNN) for fraud detection in imbalanced financial data",
      "sameAs": "https://www.nature.com/articles/s41598-025-25200-3"
    },
    {
      "@type": "CreativeWork",
      "name": "A Review of Artificial Intelligence for Financial Fraud Detection: Methods, Challenges, and Future Directions",
      "sameAs": "https://www.mdpi.com/2076-3417/16/4/1931"
    }
  ]
}