Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation

In 30 Sekunden

Was passiert ist

Research combines gradient boosting, graph features, anomaly detection, and LLM agents for explainable fraud detection on transaction data.

Warum es zählt

Matters for engineers building fraud systems where auditability and human review are required alongside detection accuracy.

Achtung

LLM investigation agent underperformed simple thresholding despite explanations and context, suggesting plausible rationales do not guarantee better decisions.