Product information
- Category: Web Application
- Operating System: Any
Data Gear Alert Suppression and Segmentation Using AI
During the past years, most of the AML systems were only depending on rule-based model scenarios, which lead to a lot of false positive alerts. False positive alerts drain the compliance time and High-risk entities can escape scrutiny. “The true cost of false positives isn’t just in the number of hours analysts or investigators spend to review unproductive work items, It is also in the drain on resources that prevents a financial institution from recognizing complex or more egregious activities that represent greater risk”
The true cost of false positives isn’t just in the number of hours analysts or investigators spend to review unproductive work items, It is also in the drain on resources that prevents a financial institution from recognizing complex or more egregious activities that represent greater risk
Segmentation Definition
- Customers based on their behavior.
- Each group has different number of customers based on the similarities of this group.
- For example, we could see large number of customers in one segment because they are near each other in the behavior.
As the number of false positive alerts are always high, compliance officers power are drained by this alerts. The need for closing this false positive alerts automatically is high without losing important alerts. Machine learning as a solution for false positive alerts DataGear Automatic suppression solution is designed based on machine learning technologies to enhance AML system. Automatic suppression system is learning from compliance previous decisions and apply it to close false positive patterns.