A contrastively pre-trained transformer on raw transaction time series is reported to improve money-laundering detection and FDR control over tabular and LSTM baselines, though the evaluation protocol limits the strength of the claim.
Rule-based anti-money laundering in financial intelligence units: Experience and vision
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Representation learning with a transformer by contrastive learning for money laundering detection
A contrastively pre-trained transformer on raw transaction time series is reported to improve money-laundering detection and FDR control over tabular and LSTM baselines, though the evaluation protocol limits the strength of the claim.