Robust Sequence-Based Self-Supervised Representation Learning for Anti-Money Laundering

As online transactions rapidly increase, money laundering has become more difficult to detect, rendering traditional rule-based algorithms inadequate for the current severe laundering landscape. Although efforts have been made to model user behavior sequences for detecting money laundering, these approaches still fall short in scenarios with extremely low anomaly rates. In our anti-money laundering practices, we have identified the following three challenges: weak perception of intensity, scarce labels, poor representation robustness. In this paper, we present CLeAR, a novel robust sequence-based self-supervised Representation Learning framework for Anti-Money Laundering. To address the weak perception of intensity, we devise an Intensity-Aware Transformer to better capture the nuances of user behavior sequences. By introducing sequence-based Contrastive Learning into this task, we effectively tackle the issue of scarce labels and enhance sequence modeling. Additionally, we developed two self-supervised learning tasks-next behavior matching and sub-sequence matching-that significantly enhance the overall robustness of representation. After rigorous experiments across datasets of various scales, CLeAR consistently delivers exceptional performance, even under the extremely low anomaly rates that closely mimic real-world conditions.