Hierarchical Risk Profiling Cube Structure
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Solution Overview
Problem
Current risk profiling methods provide only a single numerical measure of risk, requiring users to manually navigate separate data structures to understand multiple and interdependent risk factors, which is cumbersome and lacks depth in analysis.
Innovation Solution
A processor-implemented method for generating a hierarchical data structure that identifies common dimensions across risks, aggregates them into a top-level cube, and assigns risk-specific dimensions to lower-level cubes, enabling comprehensive end-user analysis and visualization of aggregated risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional risk profiling methods aggregate multiple risks into a single numerical measure, then the overall risk assessment is simplified and quick to obtain, but the user cannot understand the multiple and interdependent factors contributing to the aggregated risk without manually navigating separate data structures
Solution Approach 1:
The patent implements a hierarchical cube structure where aggregate risk measures are nested with underlying risk factors. The top-level cube contains the aggregated risk measure, while nested lower-level cubes contain specific risk factors and their drivers. This nesting allows users to drill down from the aggregate measure to understand contributing factors without manually navigating separate data structures, thus maintaining ease of operation while preserving information.
Solution Approach 2:
The patent transforms the traditional single-dimensional risk measure into a multi-dimensional cube structure. Instead of presenting risk as a single number, the system creates additional dimensions representing different risk factors, drivers, and categories. This dimensional expansion allows users to analyze risk from multiple perspectives while maintaining a unified view, eliminating the need to manually navigate separate data structures.
2Productivity
If the system provides only a single aggregate risk number, then the risk assessment process is simple and fast, but deeper understanding of multiple risk factors requires manual navigation of separate data structures
Solution Approach 1:
The patent merges multiple separate data structures containing individual risk factors into a single unified cube structure. This consolidated cube contains both the aggregate risk measure and all underlying risk factors in one integrated data structure. Users can obtain quick aggregate risk assessment while also accessing detailed risk factor information without manually navigating separate structures, thus maintaining productivity while reducing the effective complexity users must manage.
Solution Approach 2:
The unified cube structure serves multiple functions simultaneously: it provides aggregate risk measurement, displays individual risk factors, shows risk drivers, and enables drill-down analysis. This multi-functional design eliminates the need for separate data structures for different risk analysis needs, reducing the complexity users must manage while maintaining fast assessment capability.
3Measurement precision
If separate data structures are used for each risk factor, then each risk can be analyzed in detail, but users must manually navigate multiple structures to understand the overall risk profile
Solution Approach 1:
The patent nests detailed risk factor data within the unified cube structure. Each risk factor and its drivers are nested as lower-level cubes within the top-level aggregate risk cube. This nesting preserves the detailed analysis capability for each risk factor while providing a unified navigation path through the hierarchical structure, eliminating the need to manually navigate separate data structures.
Solution Approach 2:
The patent segments the unified cube into hierarchical levels: top-level aggregate risk, middle-level risk categories, and lower-level specific risk factors with drivers. This segmentation organizes detailed risk information into manageable segments that can be navigated systematically through the hierarchical structure, maintaining measurement precision while improving ease of operation through structured navigation.
Data Source
AI summary
A processor-implemented method for generating a multi-dimensional risk profiling data structure includes identifying one or more dimensions common to a plurality of risks along which all of the plurality of risks may be aggregated, assigning the one or more common dimensions to a top level cube structure, identifying a set of dimensions and risk drivers specific to each of the plurality of risks, and assigning each set of dimensions and risk drivers to each of a plurality of second level cubes.