用对比学习训练Transformer，无监督提取洗钱特征，精准识别欺诈行为。
Representation learning with a transformer by contrastive learning for money laundering detection
- 用对比学习在无标签数据上训练Transformer，学习时间序列通用表示
- 新方法在检测欺诈与非欺诈者上表现更优，误报率可控
- 适合金融风控、反洗钱场景，尤其适用于专家标注少的环境
本文针对洗钱检测问题提出新方法，利用Transformer神经网络处理包含定性与定量信息的结构化时间序列。第一步通过对比学习（无标签）学习时间序列的表示；第二步基于这些表示生成所有样本的洗钱评分。引入双阈值策略，结合Benjamini-Hochberg（BH）程序控制误报率。实验表明，该Transformer可生成通用表示，在极少领域专家干预下有效捕捉洗钱模式。相比规则系统或LSTM模型，新方法在识别欺诈者与非欺诈者方面均表现更优，同时保持低误报率。
原文摘要 · Abstract (English)
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.
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