Fraud Detection

Documentation status: guide — see Maturity and evidence.

Fraud detection is a representative hybrid reasoning problem because suspicious behavior often emerges from relations, temporal sequences, context, and rule violations rather than from one isolated record.

Approaches

• explicit business and regulatory rules;
• graph and hypergraph pattern analysis;
• temporal behavior analysis;
• anomaly detection;
• GNN/HGNN learning;
• risk scoring and ranking;
• human validation for high-impact decisions.

logiCells-specific value

The conceptual hypergraph keeps actors, transactions, roles, events, and context explicitly related. Symbolic and learned mechanisms can therefore contribute to one governed analysis while preserving a trace of why a case was flagged.