Computational Model Configuration Tool for Risk Assessment
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Solution Overview
Problem
Current systems lack an efficient method for configuring computational models to assess risk across various categories using external data sources, weight values, and threshold settings, which limits their ability to accurately calculate risk scores for entities.
Innovation Solution
A computing system that provides a tool for clients to configure computational models by selecting external data sources, specifying weight and threshold values for risk categories, and defining field types, allowing for the generation of models that calculate risk scores based on these settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational models are configured using multiple external data sources, weight values, and threshold settings, then the accuracy of risk scores is improved, but the system complexity increases
Solution Approach 1:
The system segments the risk assessment process into distinct configurable components: external data source selection, weight value assignment for different risk categories, and threshold setting for each category. This segmentation allows users to configure each aspect independently, improving accuracy while managing complexity through modular configuration.
Solution Approach 2:
The system performs preliminary configuration actions by allowing users to pre-select external data sources, pre-assign weight values to risk categories, and pre-set threshold values before actual risk assessment. This preliminary setup phase separates the complex configuration tasks from the execution phase, enabling accurate risk scoring without overwhelming complexity during operation.
2Adaptability or versatility
If a comprehensive tool for configuring computational models is provided, then the adaptability of the system is improved, but the ease of operation deteriorates
Solution Approach 1:
The system applies local quality by providing specific configuration options tailored to different risk categories (e.g., financial risk, operational risk, compliance risk). Each category can have its own weight values and thresholds, allowing the system to adapt to different assessment needs while maintaining a consistent user interface pattern that preserves ease of operation.
Solution Approach 2:
The configuration tool is designed as a universal interface that handles multiple risk categories, external data sources, and parameter types (weights, thresholds) through a single unified mechanism. This multi-functionality allows the system to adapt to various risk assessment scenarios without requiring separate tools or complex procedures for each case.
3Adaptability or versatility
If multiple risk categories with different weight values and thresholds are implemented, then the comprehensiveness of risk assessment is improved, but the device complexity increases
Solution Approach 1:
The system segments risk assessment into distinct categories (financial, operational, compliance, etc.), each with its own configurable weight values and thresholds. This segmentation allows comprehensive multi-category assessment while managing complexity by treating each category as an independent configurable unit rather than a monolithic system.
Solution Approach 2:
The system manages complexity through parameter changes by allowing users to adjust weight values and thresholds for different risk categories without changing the underlying system structure. This parameter-based configuration enables comprehensive multi-category assessment while maintaining a consistent, manageable system architecture.
Data Source
AI summary
Some embodiments provide a non-transitory machine-readable medium that stores a program. The program provides a client device a tool for configuring computational models. The program further receives, from the client device and through the tool, a selection of a set of external data sources. The program also receives, from the client device and through the tool, a plurality of weight values for a plurality of categories. The program further receives, from the client device and through the tool, a plurality of threshold values for the plurality of categories. The program also generates a plurality of computational models based on the set of external data sources, the plurality of weight values for the plurality of categories, and the plurality of threshold values for the plurality of categories.