Summary of Identifying Money Laundering Subgraphs on the Blockchain, by Kiwhan Song et al.
Identifying Money Laundering Subgraphs on the Blockchain
by Kiwhan Song, Mohamed Ali Dhraief, Muhua Xu, Locke Cai, Xuhao Chen, Arvind, Jie Chen
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: General Finance (q-fin.GN)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces RevTrack, a graph-based framework for large-scale Anti-Money Laundering (AML) analysis with lower costs and higher accuracy. It builds upon previous work that models financial transactions as graphs to identify suspicious activities. The proposed framework tracks initial senders and final receivers of funds, indicating the nature of their respective subgraphs. A neural network model, RevClassify, is developed for subgraph classification, outperforming state-of-the-art techniques in both cost and accuracy when benchmarked on Elliptic2 dataset. Additionally, RevFilter iteratively filters licit transactions to identify new suspicious subgraphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to improve Anti-Money Laundering (AML) detection by analyzing financial transactions as graphs. Current methods are expensive and require knowing which parts of the graph are suspicious. The authors propose a new approach called RevTrack that can analyze large amounts of data more efficiently and accurately. They also develop special tools, like RevClassify and RevFilter, to help identify suspicious activities. These tools can be used together to improve AML detection and prevent financial crimes. |
Keywords
» Artificial intelligence » Classification » Neural network
