标题： 基于对比学习的变压器表示学习用于洗钱检测 显示英文标题

标题： Representation learning with a transformer by contrastive learning for money laundering detection

摘要： 当前的工作解决了洗钱检测问题。 引入了一种新方法，该方法通过变压器神经网络利用定性和定量数据的结构化时间序列。 此过程的第一步旨在通过对比学习（无需任何标签）学习时间序列的表示。 第二步利用这些表示为所有观测生成洗钱评分。 然后引入了一种双阈值方法，该方法通过Benjamini-Hochberg（BH）过程确保受控的假阳性率。 实验确认变压器能够生成成功的通用表示，从而在最少领域专家监督的情况下利用洗钱模式。 它还说明了新方法在检测非欺诈者以及欺诈者方面具有更高的能力，同时保持假阳性率受控。 这与基于规则的方法或基于LSTM架构的方法形成鲜明对比。 显示英文摘要

摘要： The present work tackles the money laundering detection problem. A new procedure is introduced which exploits structured time series of both qualitative and quantitative data by means of a transformer neural network. The first step of this procedure aims at learning representations of time series through contrastive learning (without any labels). The second step leverages these representations to generate a money laundering scoring of all observations. A two-thresholds approach is then introduced, which ensures a controlled false-positive rate by means of the Benjamini-Hochberg (BH) procedure. Experiments confirm that the transformer is able to produce general representations that succeed in exploiting money laundering patterns with minimal supervision from domain experts. It also illustrates the higher ability of the new procedure for detecting nonfraudsters as well as fraudsters, while keeping the false positive rate under control. This greatly contrasts with rule-based procedures or the ones based on LSTM architectures.