Hypergraph-based contrastive learning for enhanced fraud detection.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Cybersecurity

Background:

• Digital platforms face sophisticated fraud using multi-hop attacks.
• Traditional Graph Neural Networks (GNNs) struggle with complex fraud patterns due to homophily, label imbalance, and noise.
• Existing methods fail to capture high-order relational structures in fraud networks.

Purpose of the Study:

• To develop a novel framework, the Hypergraph-based Contrastive Learning Network (HCLNet), for detecting camouflaged fraud.
• To overcome the limitations of traditional GNNs in capturing complex, high-order fraud patterns.
• To improve the accuracy and robustness of fraud detection systems in digital ecosystems.

Main Methods:

• Multi-relational hypergraph fusion to model group-wise fraud syndicates.
• Multi-head gated hypergraph aggregation for diverse pattern capture and feature balancing.
• Hierarchical dual-view contrastive learning with feature masking and topology dropout for self-supervised discrimination.

Main Results:

• HCLNet demonstrated superior performance on real-world datasets compared to baseline methods.
• Significant improvements were observed across key evaluation metrics for fraud detection.
• The model effectively revealed distinct separation patterns between fraudulent and benign entities.

Conclusions:

• HCLNet offers a powerful new approach for combating evolving camouflaged fraud tactics.
• The framework's ability to model complex relationships enhances detection capabilities.
• This research contributes to more robust fraud detection in digital environments.