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arXiv AI By Yixuan Chen, Hongyu Zhan, Jie Sheng, Weiyu Han, Shuai Chen, Tianyi Zhang, Xiao Tan, Jun Xia

LLM-Enhanced Heterogeneous Graph Learning with Business Semantics for Financial Fraud Detection

Read the original on arXiv AI →

SemHGT is a new framework that enhances heterogeneous graph learning for financial fraud detection by explicitly incorporating business semantics of structured attributes. It uses role‑aware prompts to generate risk‑related descriptions, maps these to semantic vectors, and then guides multi‑round sampling of transaction paths based on semantic similarity. The framework combines local path evidence and global neighbor summaries in a unified transformer, achieving superior performance on three benchmark datasets compared to existing baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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