{"ID":2871966,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10715","arxiv_id":"2509.10715","title":"Network Embedding Analysis for Anti-Money Laundering Detection","abstract":"We employ network embedding to detect money laundering in financial transaction networks. Using real anonymized banking data, we model over one million accounts as a directed graph and use it to refine previously detected suspicious cycles with node2vec embeddings, creating a new network parameter, the spread number. Combined with more traditional centrality measures, these define an aggregate score $R$ that highlights so-called anti-central nodes: accounts that are structurally important yet organized to avoid detection. Our results show only a small subset of cycles attain high $R$ values, flagging concentrated groups of suspicious accounts. Our approach demonstrates the potential of embedding-based network analysis to expose laundering strategies that evade traditional graph centrality measures.","short_abstract":"We employ network embedding to detect money laundering in financial transaction networks. Using real anonymized banking data, we model over one million accounts as a directed graph and use it to refine previously detected suspicious cycles with node2vec embeddings, creating a new network parameter, the spread number. C...","url_abs":"https://arxiv.org/abs/2509.10715","url_pdf":"https://arxiv.org/pdf/2509.10715v1","authors":"[\"Anthony Bonato\",\"Adam Szava\"]","published":"2025-09-12T22:12:36Z","proceeding":"cs.SI","tasks":"[\"cs.SI\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}