Transaction-Based Entity Monitoring in a Client Due Diligence Context

Current solutions to Anti-money Laundering encompass three different components, namely, transaction monitoring, screening and Customer Due Diligence. These have been mainly based on rule systems and human analysts, which can lead to many false positive alerts and a large load on human resources. In this work, we explore a novel approach to aid CDD. To do this, we propose the usage of machine learning methods to calculate an entity’s risk based on its transactional behavior by leveraging historical transactions to generate a Risk Score. First we summarize the transaction behavior into an embedding using feature engineering. Then we calculate a risk score that quantifies the dissimilarity of an entity’s behavior to what is expected using Anomaly Detection techniques. Finally, with the use of explainability techniques we clarify the assigned risk score by showing the specifics of an entity’s behavior that contributed to the final assessment of our approach. With our proposed method, we can reduce the burden on human analysts by 1) using machine-learning based techniques that can identify incorrectly classified, and therefore, potentially illicit entities by comparing their transactional behavior to other entities with the same label; and 2) generating a report with information that can provide a reasonable explanation for an assigned RS in the form of visualizations.