arXiv:2605.28524v1 Announce Type: new Abstract: In recent years, Large Language Models (LLMs) have shown great capability in processing graph tasks such as fraud detection. However, most existing methods rely heavily on rich text attributes, which poses difficulties for this domain due to the lack of textual data. Although some pioneering methods attempt to overcome it, their textualization of graph structures via hard prompts easily leads to feature distortion. Additionally, fraud detection often exhibits multi-relational complexity, where current methods struggle to capture this deep semanti
The paper addresses current limitations in applying LLMs to graph tasks like fraud detection, particularly the scarcity of rich text attributes and the challenges of multi-relational complexity within graph structures.
Improving fraud detection capabilities through innovative AI frameworks can significantly reduce financial losses and enhance security across various industries.
This framework offers a new approach to leveraging LLMs for fraud detection by effectively handling limited textual data and complex multi-relational graphs.
- · Financial institutions
- · E-commerce platforms
- · AI/ML developers
- · Cybersecurity firms
- · Fraudsters
- · Traditional fraud detection methods
Enhanced ability to identify fraudulent activities using LLMs even with sparse textual data.
Increased trust in digital transactions and financial systems due to improved security protocols.
Potential for this methodology to be adapted for other graph-based anomaly detection problems beyond fraud, such as supply chain integrity or network security.
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Read at arXiv cs.AI