Customer Segmentation in AML
Dynamic clustering methods for fewer false positives and more efficient money laundering prevention
Customer categories must be aligned with customer behaviour.
Many financial institutions rely on static customer groups with fixed thresholds for their AML monitoring. However, broad categories such as "private customers" and "corporate customers" fail to accurately reflect actual transaction behaviour. The result: a high number of false positives and inefficient monitoring processes.
Our data-driven clustering within existing customer categories enables a more refined risk assessment in line with the risk-based approach. Transaction and customer data are automatically analysed to assign customers with similar behaviour, risk profiles, or other relevant factors into dynamic segments. This classification is continuously reviewed to ensure that behavioural changes are promptly reflected in risk management. The result: Thresholds can be precisely adjusted to identify suspicious cases more accurately and minimize false alarms.
Our clustering approach has been developed for Siron® AML but can also be adapted for use in other AML monitoring systems. Thanks to the explainable statistical methodology, the approach remains fully transparent and meets regulatory requirements.
Capabilities & features
Our dynamic customer segmentation solution helps you enhance the precision and efficiency of your AML monitoring:
✔ Automatic identification of customer groups based on actual transaction behaviour
✔ Combination of customer master data and behavioural data for fine-grained segmentation
✔ Continuous review and adjustment of segment assignments based on behavioural changes
✔ Dynamic thresholds per cluster for more effective rule-based monitoring
✔ Flexible integration into existing AML systems
Your advantages
Differentiated customer segments enable more precise analysis and tailored measures
Reduction of unnecessary alerts through realistic clustering
Focus on actual risks instead of irrelevant alerts
Regular updates to segments in response to changing behaviour patterns
Statistically sound, explainable methodology