Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs
This work addresses the costly inefficiencies in anti-money laundering operations for financial institutions, representing an incremental improvement over existing rule-based systems.
The paper tackles the problem of high false-positive rates in anti-money laundering alert systems by proposing a machine learning triage model that uses entity-centric and graph-based features, achieving an 80% reduction in false positives while detecting over 90% of true positives on a real-world banking dataset.
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.