arXiv AI
6d ago

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

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.

By Yixuan Chen, Hongyu Zhan, Jie Sheng, Weiyu Han, Shuai Chen, Tianyi Zhang, Xiao Tan, Jun Xia
arXiv Machine Learning
Oct 22

Leveraging Time-Series Foundation Models for Financial Crime Detection

The paper introduces Temporal-Context Edge Enrichment (TCEE), a framework that uses frozen time‑series foundation models (TSFMs) to summarise each account’s transaction history and enrich transaction representations with temporal context. These enriched edges can be directly scored for classification or used as features in a graph neural network (GNN). Across six AMLWorld datasets, TCEE improves PR‑AUC by an average of 10.8% over the best baseline and trains 6.0× faster; when combined with a GNN, it yields an additional 4.8% improvement on smaller datasets and a 14.5% gain on an Ethereum phishing dataset.

By Danae Natalie Savvidi, H. \c{C}a\u{g}r{\i} Bilgi, Kubilay Atasu, Arie van Deursen