Improving Money Laundering Detection for a Leading Dutch Bank

We have developed a machine learning model for a leading Dutch bank with over EUR 300 billion in assets to detect potential money laundering activities within its high-net-worth client segment.

Challenge​

Anti-Money Laundering (AML) models typically struggle with a common issue: a limited number of true positive cases for effective model training. To address this, most AML models incorporate some form of anomaly detection to identify unusual patterns in client behavior.​

We focused on the bank’s wealthiest clients, identified by a minimum asset threshold. This presents a unique challenge because, by nature, these clients are already statistical outliers. As a result, we needed to identify anomalies within this group of outliers, significantly increasing the model's complexity.​

The bank has an existing model in place, and our role is to enhance its performance, with a focus on:

• Redeveloping peer groups
• Reducing false positives in AML detection

Model Development

Like most machine learning projects, the development lifecycle is divided into three key phases: feature engineering (which takes up most of the time), modeling, and testing/implementation.​

We designed model features to ensure that normal client behavior corresponds to lower values, while anomalous behavior triggers higher values. This approach enhances the effectiveness of anomaly detection models.​

Collaborating closely with operations analysts, we refined these features to minimize obvious false positives among top-scoring cases. As a result, clients with legitimate activities are less likely to receive high anomaly scores in the final model.​

Peer Groups​

Detecting anomalous behavior among high-net-worth clients—who are all outliers by nature and exhibit highly diverse transaction patterns—requires a nuanced approach. To address this, we grouped clients based on their transaction behaviors to form peer groups.​

Key features were then evaluated by measuring how much a client’s behavior deviated from that of their peers. This method identifies anomalies by comparing clients to peers with similar transaction patterns.​

Our role involved revisiting and refining these peer groups to enhance the effectiveness of peer-based features, ultimately improving the model’s overall performance.

For more information, visit our Financial Crime Prevention page, or reach out to Johannes Lont, Senior Manager.