Neural Network Optimization for Risk Assessment Monotonicity
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Solution Overview
Problem
Automated modeling systems face challenges in accurately assessing risks and generating explanatory data due to constraints such as monotonicity and multicollinearity, which can lead to reduced predictive accuracy and exclusion of relevant predictor variables.
Innovation Solution
A model development engine optimizes neural networks by performing factor analysis to identify common and specific factors that satisfy monotonicity and multicollinearity constraints, allowing for the inclusion of more predictor variables and improving predictive accuracy while maintaining explanatory capability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional automated modeling algorithms are used to assess risks, then the system can generate predictive outputs, but the predictive accuracy is reduced and relevant predictor variables are excluded due to monotonicity and multicollinearity constraints
Solution Approach 1:
The patent transforms the neural network model parameters and structure to accommodate monotonicity and multicollinearity constraints. By modifying the model parameters and introducing constraint satisfaction mechanisms, the system achieves both high predictive accuracy and the ability to include relevant predictor variables that previously would have been excluded.
Solution Approach 2:
The patent implements dynamic optimization processes that iteratively adjust the neural network model to satisfy constraints while maintaining predictive performance. The system dynamically balances between incorporating diverse predictor variables and adhering to monotonicity and multicollinearity requirements through continuous refinement.
2Reliability
If neural networks are optimized to satisfy monotonicity and multicollinearity constraints, then explanatory capability is maintained, but the model complexity increases
Solution Approach 1:
The patent segments the model optimization process into distinct components: constraint identification, constraint satisfaction mechanisms, and predictive performance optimization. This segmentation allows the system to manage complexity by addressing each aspect separately while maintaining overall coherence and explanatory capability.
Solution Approach 2:
The patent introduces intermediary mechanisms that mediate between the neural network's predictive functions and the monotonicity/multicollinearity constraints. These intermediaries facilitate the translation of constraint requirements into model adjustments without requiring complete model redesign, thus managing complexity while preserving explanatory power.
3Measurement precision
If factor analysis is performed to identify common factors, then multicollinearity is reduced, but the computational processing time increases
Solution Approach 1:
The patent performs factor analysis and identifies common factors as a preliminary step before the main model training process. By pre-processing the predictor variables to reduce multicollinearity beforehand, the system minimizes computational overhead during subsequent training iterations and achieves faster overall processing time.
Solution Approach 2:
The patent extracts common factors from correlated predictor variables through factor analysis, separating the multicollinearity issue from the main modeling process. This extraction reduces the dimensionality and inter-correlation of input variables, leading to more efficient training and reduced computational time in the subsequent model optimization stages.
Data Source
AI summary
Certain aspects involve optimizing neural networks or other models for assessing risks and generating explanatory data regarding predictor variables used in the model. In one example, a system identifies predictor variables. The system generates a neural network for determining a relationship between each predictor variable and a risk indicator. The system performs a factor analysis on the predictor variables to determine common factors. The system iteratively adjusts the neural network so that (i) a monotonic relationship exists between each common factor and the risk indicator and (ii) a respective variance inflation factor for each common factor is sufficiently low. Each variance inflation factor indicates multicollinearity among the common factors. The adjusted neural network can be used to generate explanatory indicating relationships between (i) changes in the risk indicator and (ii) changes in at least some common factors.