BlazingAML uses a multi-stage graph mining framework and compiler to express fuzzy AML patterns, matching SOTA F1 scores while delivering 210x CPU and 333x GPU speedups on IBM 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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BlazingAML: High-Throughput Anti-Money Laundering (AML) via Multi-Stage Graph Mining
BlazingAML uses a multi-stage graph mining framework and compiler to express fuzzy AML patterns, matching SOTA F1 scores while delivering 210x CPU and 333x GPU speedups on IBM 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.