TransXion supplies a 3-million-transaction graph benchmark with profile-aware normal activity and stochastic illicit subgraphs that produces lower detection scores than prior AML datasets.
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ExSTraQt uses quasi-temporal graph representations and supervised learning to detect suspicious transactions, achieving F1 score uplifts of up to 1% on real data and over 8% on synthetic datasets compared to prior AML models.
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TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering
TransXion supplies a 3-million-transaction graph benchmark with profile-aware normal activity and stochastic illicit subgraphs that produces lower detection scores than prior AML datasets.
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Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation
ExSTraQt uses quasi-temporal graph representations and supervised learning to detect suspicious transactions, achieving F1 score uplifts of up to 1% on real data and over 8% on synthetic datasets compared to prior AML models.