Money laundering is a global problem that concerns legitimizing proceeds from serious felonies (1.7-4 trillion euros annually) such as drug dealing, human trafficking, or corruption. The anti-money laundering systems deployed by financial institutions typically comprise rules aligned with regulatory frameworks. Human investigators review the alerts and report suspicious cases. Such systems suffer from high false-positive rates, undermining their effectiveness and resulting in high operational costs. We propose a machine learning triage model, which complements the rule-based system and learns to predict the risk of an alert accurately. Our model uses both entity-centric engineered features and attributes characterizing inter-entity relations in the form of graph-based features. We leverage time windows to construct the dynamic graph, optimizing for time and space efficiency. We validate our model on a real-world banking dataset and show how the triage model can reduce the number of false positives by 80% while detecting over 90% of true positives. In this way, our model can significantly improve anti-money laundering operations.
翻译:洗钱是一个全球性问题,涉及将贩毒、人口贩运或腐败等严重重罪(每年1.7至4万亿欧元)所得收入合法化。金融机构部署的反洗钱系统通常包含与监管框架相一致的规则。调查人员审查警报并报告可疑案件。这类系统存在高假阳率,损害其效力并导致高运作成本。我们提议了一个机器学习分级模式,以补充基于规则的系统,并学习准确预测警报风险。我们的模型使用实体中心设计的特征和特征,以图表为形式的实体间关系特征。我们利用时间窗口构建动态图形,优化时间和空间效率。我们在现实世界银行数据集上验证了我们的模型,并展示了三角模型如何将假正数减少80%,同时发现90%以上的真实正数。这样,我们的模型就可以大大改进反洗钱行动。