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.
Large-scale machine learning with stochastic gradient descent
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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.