arXiv:2606.28134v1 Announce Type: new Abstract: Graph-based fraud detection is essential for safeguarding large-scale transaction systems, where undetected anomalies may lead to substantial financial losses and security risks. Real-world fraud graphs pose two coupled challenges: sparse and imbalanced supervision, where verified fraudulent labels are scarce and heavily skewed toward benign accounts, and representation dilution, where spatial message passing may oversmooth camouflaged anomalies while spectral filters may suppress fraud-relevant mid- and high-frequency irregularities. To address
The proliferation of digital transactions and increasingly sophisticated fraud techniques necessitates advanced AI methods that can overcome data limitations inherent in real-world fraud detection systems.
Improved fraud detection, especially in challenging data environments, protects financial systems and large transaction networks from significant losses and security breaches, enhancing trust and operational efficiency.
The development of diffusion-guided learning techniques for graph neural networks offers a more robust approach to identifying camouflaged anomalies and addressing sparse, imbalanced fraud data.
- · Financial institutions
- · E-commerce platforms
- · AI/ML security firms
- · Consumers
- · Fraudsters
- · Traditional rule-based fraud detection systems
Reduced financial losses due to fraud and improved security in large-scale transaction systems.
Increased trust in digital transaction platforms could accelerate their adoption and expansion into new markets.
The methodology could be adapted to other anomaly detection problems beyond finance, impacting cybersecurity or industrial monitoring.
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Read at arXiv cs.LG