标题： 欺骗的形状：洗钱模式中的行为一致性和脆弱性 显示英文标题

标题： The Shape of Deceit: Behavioral Consistency and Fragility in Money Laundering Patterns

摘要： 传统的反洗钱（AML）系统主要关注识别异常实体或交易，根据统计偏差或可疑行为对其进行标记以供人工调查。 然而，这种范式误解了洗钱的真实性质，洗钱很少是异常的，而是通常有意识的、重复的，并隐藏在一致的行为模式中。 在本文中，我们挑战以实体为中心的方法，提出一种基于网络理论的视角，强调在定向交易网络中检测预定义的洗钱模式。 我们引入行为一致性作为洗钱活动的核心特征，并认为这些模式通过表达语义和功能角色的子图结构来更好地捕捉，而不仅仅是几何结构。 关键的是，我们探讨了模式脆弱性的概念：洗钱模式对小属性变化的敏感性，以及在剧烈拓扑变换下仍保持的语义鲁棒性。 我们认为，洗钱检测不应依赖于统计异常值，而应关注行为本质的保留，并提出基于这一见解的模式相似性再概念化。 这种哲学和实践上的转变对AML系统如何建模、扫描和解释网络以打击金融犯罪具有重要意义。 显示英文摘要

摘要： Conventional anti-money laundering (AML) systems predominantly focus on identifying anomalous entities or transactions, flagging them for manual investigation based on statistical deviation or suspicious behavior. This paradigm, however, misconstrues the true nature of money laundering, which is rarely anomalous but often deliberate, repeated, and concealed within consistent behavioral routines. In this paper, we challenge the entity-centric approach and propose a network-theoretic perspective that emphasizes detecting predefined laundering patterns across directed transaction networks. We introduce the notion of behavioral consistency as the core trait of laundering activity, and argue that such patterns are better captured through subgraph structures expressing semantic and functional roles - not solely geometry. Crucially, we explore the concept of pattern fragility: the sensitivity of laundering patterns to small attribute changes and, conversely, their semantic robustness even under drastic topological transformations. We claim that laundering detection should not hinge on statistical outliers, but on preservation of behavioral essence, and propose a reconceptualization of pattern similarity grounded in this insight. This philosophical and practical shift has implications for how AML systems model, scan, and interpret networks in the fight against financial crime.