Resource / AML Software Implementation & Production Monitoring with iceDQ DataOps Platform

iceDQ accelerates AML software implementation and prevents false positive signals in AML operations.

AML software is a downstream system that consumes data from multiple sources. AML software analyzes data based on compliance models.  This results in suspicious activity reports. Further, they also monitor data for regulations such as FATCA, trade restrictions, sanctions, and watch list.

However, if the incoming data is not good then these results cannot be relied upon. It is basically at the mercy of upstream systems.

iceDQ is an in-memory data audit rules engine.  It sits between the data sources and downstream systems such as ALM.  It can validate and reconciliation data coming from multiple data sources. Thus, managing data issues before it affect the downstream system.

iceDQ for QA Automation – Development Accelerator
	During the Implementation phase of AML software, the focus is on quality assurance of the data inputs. Most AML software companies use set of SQLs to validate the data. However, these are custom SQLs, disorganized and executed manually. With iceDQ these rules can be prepackaged into a rules repository and re-used for QA at different client implementations.

iceDQ for QA Automation – Development Accelerator

During the Implementation phase of AML software, the focus is on quality assurance of the data inputs. Most AML software companies use set of SQLs to validate the data. However, these are custom SQLs, disorganized and executed manually. With iceDQ these rules can be prepackaged into a rules repository and re-used for QA at different client implementations.

iceDQ as a Gatekeeper – Production

Absence of iceDQ as a Data Gatekeeper results in many of False Positive with AML Models. Time and money will also be wasted in analyzing these results for false positives.

In conclusion, iceDQ DataOps platform not only helps accelerate AML implementation but also an effective gatekeeper for all data issues (in production) from upstream systems, thus avoiding false positives generated by the execution of AML models.