Bridging Cognitive Neuroscience and Graph Intelligence: Hippocampus-Inspired Multi-View Hypergraph Learning for Web Finance Fraud

• URL: http://arxiv.org/abs/2601.11073v1
• Date: Fri, 16 Jan 2026 08:18:23 GMT
• Title: Bridging Cognitive Neuroscience and Graph Intelligence: Hippocampus-Inspired Multi-View Hypergraph Learning for Web Finance Fraud
• Authors: Rongkun Cui, Nana Zhang, Kun Zhu, Qi Zhang,
• Abstract summary: HIMVH is a Hippocampus-Inspired Multi-View Hypergraph learning model for web finance fraud detection.<n>Inspired by the scene conflict monitoring role of the hippocampus, we design a cross-view inconsistency perception module.<n>We also introduce a novelty-aware hypergraph learning module that measures feature deviations from neighborhood expectations.
• Score: 10.498542622240285
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Online financial services constitute an essential component of contemporary web ecosystems, yet their openness introduces substantial exposure to fraud that harms vulnerable users and weakens trust in digital finance. Such threats have become a significant web harm that erodes societal fairness and affects the well being of online communities. However, existing detection methods based on graph neural networks (GNNs) struggle with two persistent challenges: (1) fraud camouflage, where malicious transactions mimic benign behaviors to evade detection, and (2) long-tailed data distributions, which obscure rare but critical fraudulent cases. To fill these gaps, we propose HIMVH, a Hippocampus-Inspired Multi-View Hypergraph learning model for web finance fraud detection. Specifically, drawing inspiration from the scene conflict monitoring role of the hippocampus, we design a cross-view inconsistency perception module that captures subtle discrepancies and behavioral heterogeneity across multiple transaction views. This module enables the model to identify subtle cross-view conflicts for detecting online camouflaged fraudulent behaviors. Furthermore, inspired by the match-mismatch novelty detection mechanism of the CA1 region, we introduce a novelty-aware hypergraph learning module that measures feature deviations from neighborhood expectations and adaptively reweights messages, thereby enhancing sensitivity to online rare fraud patterns in the long-tailed settings. Extensive experiments on six web-based financial fraud datasets demonstrate that HIMVH achieves 6.42\% improvement in AUC, 9.74\% in F1 and 39.14\% in AP on average over 15 SOTA models.

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