Trapeze Mortgage Analytics
The true measure of any automation technology is how accurate and reliable it is in a production environment. If humans must constantly review and correct the system, then any anticipated efficiency gains are lost.
At SoftWorks AI’s we strive to deliver solutions that truly automate complex business process. Our Trapeze for Mortgage Automation is a purpose-built solution that streamlines many aspects of the mortgage lifecycle from origination to post-close review. In order to achieve the highest accuracy rates in the industry, Trapeze leverages advanced computer vision and machine learning to identify opportunities to identify opportunities for the system to improve over time.
Real-Time Reporting
The Trapeze Mortgage Analytics platform tracks every aspect of the mortgage automation workflow, providing our clients with real time data on the overall effectiveness of their operations. Mortgage Analytics can analyze thousands of data points to provide a holistic view of your system’s performance and pinpoint where additional productivity gains can be realized.
Our data analytics dashboard enables users to monitor the percentage of documents and fields that do not require human review. This capability provides both real-time and historical analysis to understand the overall effectiveness of Trapeze for Mortgage Automation.
Key Performance Indicators
Trapeze Mortgage Analytics is a robust platform that allows organizations to gain tremendous insight into their mortgage operations workflow. Thousands of unique data points are captured and are then available for database-driven query and presentation.
Examples
Accuracy Rate
Applies to the accuracy of classification and data extraction, respectively. The solution analyzes the ratio of correctly classified document pages or (correctly extracted data fields) without user correction vs.
Passthrough Rate
The
False Flag Rate
This indicator analyzes the percentage of classified document pages or (extracted data fields) mistakenly flagged by the system for user review with correct classification (or extraction).

