Quantum-Inspired Contextual Learning for Sparse-Ring Fraud Detection in Dynamic Transaction Graphs
We present an exploratory benchmark and quantum-inspired modeling prototype for fraud screening in dynamic financial transaction graphs. Coordinated fraud may not be visible from individual transactions alone, but may emerge as a multi-period relational pattern. We focus on sparse-ring fraud, a stylized pattern in which a completed directed cycle is distributed across several days, requiring models to integrate evidence across both time and graph structure. We study this problem using a synthetic transaction simulator with completed sparse-ring injections and broken-ring decoys. Daily directed transaction graphs are aggregated into rolling windows and represented using raw graph features,...
| Authors | Behnam Tonekaboni, Hiroshi Yamauchi |
|---|---|
| Primary category | cs.LG |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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