AML EUC Model Management Suite

Manage End User Computing Models

AML EUC (End User Computing) Management Suite provide centralized version controls and auditable testing evidence. It also seamlessly converts the Excel logic into web APIs via simple drag-and-drop actions.

Background

Model Management is at the heart of AML Compliance. While most financial institutions built extensive policies, processes, and procedures around commercial solutions such as Transaction Monitoring and Case Management software systems, small utility programs in most cases are still critical components of the whole compliance model ecosystem that are indispensable. These are mostly MS EXCEL programs fulfilling small yet important tasks that are required to keep AML models such as TM scenarios and segmentation models performing. 

Challenges and Solutions

These EUCs are mostly managed by analytical team members rather than IT teams which means they are not documented and tested as rigorously as models running in the TM systems and Case Management systems. From the model governance point of view:

1. EUC is usually scattered around: the solution is to provide a model inventory to keep track of these utilities
2. Lack of standardized testing performed: EUC is managed by analytics team members, and usually sits on the desktop of one analyst. The solution here is to standardize testing and evidence testing performed.
3. Lack of documentation for model validation purposes: the solution here is to provide documentation through model purposes, owner, testing and results
4. Lack of Data Quality checks: the solution is to provide a standard UI (user interface) to show data quality checks and document data used.

Details

Powered by a cutting-edge technology platform, EUC EXCELs can be readily converted into Business logic and APIs, which the AML EUC Model Management Suite utilize to create GUI for model analysts to easily fulfil major model validation requirements including but not limited to:

1. Model inventory
2. Testing
3. Documentation
4. Data Quality