Dynamic Graphical User Interface for Model Risk Monitoring
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Solution Overview
Problem
Existing technologies fail to dynamically isolate, identify, and illustrate factors contributing to model drift and model risk, which poses a danger of inaccurate modeling due to their reliance on static graphical user interfaces.
Innovation Solution
A system and method for dynamic monitoring through graphical user interfaces that includes a processor and data storage, capable of generating graphical illustrations to highlight selected factors and their contributions to model risk and drift, allowing for interactive identification and visualization of factors impacting model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static graphical user interfaces are used for model monitoring, then the system complexity is reduced, but the ability to dynamically isolate, identify, and illustrate factors contributing to model drift and risk is lost
Solution Approach 1:
The graphical user interface transitions from a static display to a dynamic interactive system that responds to user selections. The interface dynamically recolors graphical illustrations to highlight selected factors, dynamically generates detailed views based on user interactions, and dynamically updates visual representations of model risk and drift. This dynamic behavior enables the interface to adapt to different monitoring needs while maintaining a consistent underlying structure.
Solution Approach 2:
The monitoring interface is divided into multiple selectable factors, each representing a distinct aspect of model risk. Users can select individual factors for detailed examination, and the system segments the overall model risk into contributable components. This segmentation allows users to focus on specific areas of concern without being overwhelmed by the complete set of factors.
2Measurement precision
If static graphical user interfaces are used, then ease of operation is maintained, but the precision of factor identification and illustration is insufficient
Solution Approach 1:
The interface adds an interactive dimension to the monitoring system by incorporating selectable features and dynamic responses. Users can select factors and time periods, triggering detailed views that provide deeper insights. This additional interactive dimension enhances measurement precision without significantly complicating the basic operation, as the core interface remains accessible while offering advanced capabilities on demand.
Solution Approach 2:
The interface implements a nested structure where a general overview of model risk contains selectable factors, which in turn contain detailed views with specific measurements and illustrations. This nested organization allows users to drill down from high-level monitoring to detailed factor analysis while maintaining a clear hierarchical structure. The nesting enables precise factor identification through progressive disclosure of information.
3Reliability
If dynamic graphical user interfaces are implemented to highlight selected factors, then model risk analysis capability is improved, but the device complexity increases
Solution Approach 1:
The system creates visual copies and representations of model factors through graphical illustrations. When a factor is selected, the system generates a detailed view that copies and expands upon the original factor representation. This copying approach allows the system to maintain the original simplified interface structure while creating enhanced visual representations for analysis, thereby improving reliability without proportionally increasing system complexity.
Solution Approach 2:
The interface uses color changes as a visual mechanism to highlight selected factors and indicate their contribution to model risk. Different colors or shading intensities represent different levels of risk contribution or factor selection states. This visual encoding technique improves model risk detection accuracy by making critical information immediately apparent, while relying on established visual perception principles rather than complex computational mechanisms.
Data Source
AI summary
Methods and systems for monitoring through graphical user interfaces are disclosed. In one aspect, a system is disclosed that includes a processor and data storage including instructions that, when executed by the processor, cause the system to perform operations. The operations include maintaining an input file including predetermined criteria for a plurality of factors, receiving a model dataset generated using a model, based on the input file and the model dataset, generating a first graphical user interface that includes a graphical illustration of a subset of the plurality of factors, a model risk for the model, and a selectable feature associated with a selected factor in the subset and a selected time period. The operations further include receiving through the first graphical user interface a selection of the selectable feature, and, in response, generating a second graphical user interface that recolors a portion of the first graphical user interface.