TMR-GGNN: Credit Card Fraud Detection based on Time-Aware Multi-Relational Guided Graph Neural Network
arXiv:2606.18444v1 Announce Type: cross Abstract: In recent years, credit card fraud detection has faced significant challenges due to highly imbalanced data, evolving fraud patterns, and complex relational structures among transaction entities. To address these issues, this research proposes a novel framework called Timeaware Multi Relational Guided Graph Neural Network (TMR GGNN). Particularly, the proposed TMR GGNN extends the encoder decoder Graph Neural Network GNN architecture by modeling heterogeneous interactions across customers, merchants, devices, and IPs over temporal windows. Subs
The increasing sophistication of financial fraud necessitates continuous innovation in detection methods, leveraging advancements in AI, particularly GNNs, to handle complex relational data.
Improved fraud detection systems reduce financial losses for institutions and consumers, fostering greater trust in digital transactions and potentially reducing operational costs for financial services.
This research introduces a more robust and adaptable AI framework for identifying credit card fraud, addressing limitations of previous models in handling imbalanced data and evolving fraud patterns.
- · Financial Institutions
- · E-commerce Platforms
- · Consumers
- · AI/ML Security Firms
- · Fraud rings
- · Legacy fraud detection systems
Financial institutions can deploy more effective fraud detection models, leading to fewer successful fraudulent transactions.
Reduced fraud losses may enable banks to offer lower fees or better interest rates to customers, while AI security firms gain market share.
The development of highly adaptive fraud AI could inadvertently lead to an arms race with fraudsters, pushing them towards more advanced obfuscation techniques.
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Read at arXiv cs.AI