Why Your AML Segmentation Isn’t Enough
You're using an AML solution with rules and scenarios set up during the implementation project, generating alerts when transactions exceed certain thresholds. Maybe you've even segmented customers into broad categories like "corporate clients" and "private individuals" to fine-tune those thresholds.
Feels advanced, right?
Not quite.
Are all private individuals the same? Do all companies behave identically?
Spoiler: They don’t.
Setting thresholds based on broad categories alone is like using the same speed limit for bicycles and sports cars – it doesn’t make sense, and the regulator won’t favour this approach.
Our clustering approach for Siron®AML goes beyond these one-size-fits-all categories.
By analysing customer transaction behaviour, we uncover meaningful subgroups within each category. Think of it as introducing "sports car companies" and "bicycle companies" instead of just "companies".
We use advanced statistical methods: first, we identify the correlation dimensions, then compute a suitable number of clusters. There’s no AI involved, so everything is explainable – even to the auditor.
With this refined segmentation, you can:
- Set more precise thresholds
- Reduce false positives (fewer pointless alerts!)
- Improve detection of truly suspicious activity
The next step: Backtesting
Once the new subcategories are defined, the logical next step is backtesting. We help you fine-tune thresholds for these newly identified groups, ensuring your AML system is not only compliant but also efficient.
