Evidence-grounded LLM triage with structured contracts and counterfactual validation achieves PR-AUC 0.75 and high faithfulness scores on synthetic AML benchmarks.
Transaction Monitoring in Anti -Money Laundering: A Qualitative Analysis and Points of View from Industry
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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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Explainable AML Triage with LLMs: Evidence Retrieval and Counterfactual Checks
Evidence-grounded LLM triage with structured contracts and counterfactual validation achieves PR-AUC 0.75 and high faithfulness scores on synthetic AML benchmarks.
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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.