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.

翻译：在线金融服务是当代网络生态系统的重要组成部分，但其开放性带来了严重的欺诈风险，损害弱势用户利益并削弱数字金融信任。此类威胁已成为侵蚀社会公平、影响在线社区福祉的重大网络危害。然而，现有基于图神经网络（GNNs）的检测方法面临两大持续挑战：（1）欺诈伪装，即恶意交易模仿良性行为以逃避检测；（2）长尾数据分布，使罕见但关键的欺诈案例难以识别。为填补这些空白，我们提出HIMVH——一种面向网络金融欺诈检测的海马体启发的多视图超图学习模型。具体而言，受海马体场景冲突监控功能的启发，我们设计了跨视图不一致性感知模块，用于捕捉多交易视图间的细微差异与行为异质性。该模块使模型能够识别微妙的跨视图冲突，从而检测在线伪装欺诈行为。此外，受CA1区匹配-失配新颖性检测机制的启发，我们引入了新颖性感知超图学习模块，通过度量特征与邻域期望的偏差并自适应重加权消息传递，从而增强对长尾场景中在线罕见欺诈模式的敏感性。在六个基于网络的金融欺诈数据集上的大量实验表明，相较于15个前沿模型，HIMVH在AUC、F1和AP指标上平均分别提升了6.42%、9.74%和39.14%。