Graph-Driven Cross-Industry Real-Time Monitoring Framework for Anti-Money Laundering Detection in Converged Mobility-Energy Supply Chain Networks
A new framework for detecting money laundering in the mobility-energy supply chain has been proposed. This graph-driven approach enhances real-time monitoring and improves detection capabilities. Experimental results indicate a significant performance improvement over existing methods.
- ▪The proposed framework is called the Graph-Driven Cross-Industry Real-Time Monitoring Framework (GCRMF).
- ▪It utilizes a cross-industry heterogeneous graph to integrate various industry semantics.
- ▪The framework shows an improvement of over 17.8% in F1 score compared to existing graph neural network methods.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Machine Learning arXiv:2605.18844 (cs) [Submitted on 13 May 2026] Title:Graph-Driven Cross-Industry Real-Time Monitoring Framework for Anti-Money Laundering Detection in Converged Mobility-Energy Supply Chain Networks Authors:Rong Liu, Xiaojun Xiao, Zhanqing Su View a PDF of the paper titled Graph-Driven Cross-Industry Real-Time Monitoring Framework for Anti-Money Laundering Detection in Converged Mobility-Energy Supply Chain Networks, by Rong Liu and 2 other authors View PDF Abstract:With the deep integration of the travel and energy industries, cross-industry supply chain finance has gradually become a high-risk field of hidden money laundering incidents.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
