Multi-Stage Model Validation with Selective Variable Rejection
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Solution Overview
Problem
Financial investment systems face challenges in accurately predicting client behavior due to the large volume of client data, which reduces model accuracy, and existing model validation methods are inadequate for dynamically scoring and validating models.
Innovation Solution
A model validation system that reduces the amount of relevant data by identifying significant variables for client behavior events, dynamically scores and validates models at multiple stages, and provides a user interface for accessing model inputs, outputs, and validation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large volume of client data is used for prediction, then more comprehensive analysis is achieved, but model accuracy decreases
Solution Approach 1:
The patent extracts and identifies significant variables from the large volume of client data using automated model validation systems. The system processes comprehensive data sets but selectively extracts only the most relevant variables that contribute to accurate predictions, thereby maintaining model accuracy while utilizing large data volumes.
Solution Approach 2:
The automated model validation system acts as an intermediary between the large volume of client data and the prediction models. It validates and scores models by processing the comprehensive data sets and identifying significant variables, thereby enabling accurate predictions without directly exposing the models to the full complexity of the large data volume.
2Reliability
If comprehensive model validation is performed, then model reliability is improved, but computational efficiency decreases
Solution Approach 1:
The model validation process is segmented into multiple stages with different validation criteria. The system performs comprehensive validation by dividing the process into distinct phases, each evaluating specific aspects of model performance, thereby achieving thorough validation while managing computational resources efficiently.
Solution Approach 2:
The validation system dynamically adjusts validation parameters and thresholds based on the specific model being evaluated. By changing validation parameters adaptively, the system performs comprehensive validation when needed while maintaining computational efficiency through optimized validation criteria for different model types and scenarios.
3Measurement precision
If multiple stages of model execution are run, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary validation and variable identification in advance before final model execution. By pre-processing and pre-validating models and identifying significant variables beforehand, the system reduces the time required during actual prediction while maintaining multi-stage execution for accuracy.
Solution Approach 2:
The automated validation system identifies and skips unnecessary validation stages for models that have already been validated or for straightforward prediction scenarios. This allows the system to maintain comprehensive multi-stage validation when needed while reducing processing time by bypassing redundant stages in appropriate cases.
Data Source
AI summary
A model management system for validating models for predicting a client behavior event includes a model validation system configured to execute each of multiple stages of a selected model, which includes calculating a respective output based on a respective input of each of the stages. The model validation system is configured to separately verify, based on the respective outputs, each of the stages. The model validation system is configured to generate and store model data corresponding to the respective outputs of each of the stages. The model data includes a first set of model data and a second set of model data different from the first set of model data. A user interface module is configured to receive an indication of credentials of a user and selectively retrieve and display one of the first set of model data and the second set of model data based on the indication.