Bridging Cognitive Neuroscience and Graph Intelligence: Hippocampus-Inspired Multi-View Hypergraph Learning for Web Finance Fraud
arXiv:2601.11073v3 Announce Type: replace 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) long-tailed data distributions, which obscure rare but critical fraudulent cases, a
The paper leverages recent advancements in graph intelligence and draws inspiration from cognitive neuroscience, specifically the hippocampus, to address persistent challenges in deep learning for fraud detection.
Improving fraud detection in online financial services is crucial for maintaining trust in digital economies and mitigating significant web harms that erode societal fairness.
New methodologies combining biological insights with graph learning could significantly enhance the accuracy and robustness of fraud detection, especially for rare, complex cases.
- · Financial services sector
- · Cybersecurity companies
- · AI/ML researchers in graph intelligence
- · End-users of online financial services
- · Fraudsters and organized cybercrime networks
- · Existing less sophisticated fraud detection platforms
More effective identification and prevention of online financial fraud.
Increased consumer confidence in digital financial platforms leading to broader adoption of online services.
The development of 'hippocampus-inspired' AI architectures could become a general paradigm for tackling complex, sparse-data problems across various domains.
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Read at arXiv cs.LG