{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/scalable-graph-learning-for-anti-money","title":"Scalable Graph Learning for Anti-Money Laundering: A First Look","arxiv_id":"1812.00076","date":"2018-11-30","proceeding":null,"authors":["Mark Weber","Jie Chen","Toyotaro Suzumura","Aldo Pareja","Tengfei Ma","Hiroki Kanezashi","Tim Kaler","Charles E. Leiserson","Tao B. Schardl"],"abstract":"Organized crime inflicts human suffering on a genocidal scale: the Mexican\ndrug cartels have murdered 150,000 people since 2006, upwards of 700,000 people\nper year are \"exported\" in a human trafficking industry enslaving an estimated\n40 million people. These nefarious industries rely on sophisticated money\nlaundering schemes to operate. Despite tremendous resources dedicated to\nanti-money laundering (AML) only a tiny fraction of illicit activity is\nprevented. The research community can help. In this brief paper, we map the\nstructural and behavioral dynamics driving the technical challenge. We review\nAML methods, current and emergent. We provide a first look at scalable graph\nconvolutional neural networks for forensic analysis of financial data, which is\nmassive, dense, and dynamic. We report preliminary experimental results using a\nlarge synthetic graph (1M nodes, 9M edges) generated by a data simulator we\ncreated called AMLSim. We consider opportunities for high performance\nefficiency, in terms of computation and memory, and we share results from a\nsimple graph compression experiment. Our results support our working hypothesis\nthat graph deep learning for AML bears great promise in the fight against\ncriminal financial activity.","url_abs":"http://arxiv.org/abs/1812.00076v1","url_pdf":"http://arxiv.org/pdf/1812.00076v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"scalable-graph-learning-for-anti-money","repo_url":"https://github.com/IBM/AMLSim","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"scalable-graph-learning-for-anti-money","repo_url":"https://github.com/tonyPo/AMLSim_prep","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"graph-learning","task_name":"Graph Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.00076","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}