Selective Data Rejection for Accurate Model Validation
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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 management system that includes a model validation system to execute multiple stages of model validation, reduce relevant variables, and provide selective access to model data through a user interface, allowing for dynamic scoring and validation of models based on thresholds and real-time client data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all client data is used in models, then the volume of data is large, but model accuracy reduces
Solution Approach 1:
The system extracts and removes irrelevant variables from the client data before model training. The variable reduction module identifies and eliminates non-predictive features, retaining only the most relevant variables that contribute to accurate client behavior predictions, thereby resolving the contradiction between data volume and model accuracy
Solution Approach 2:
The system applies different processing quality to different variables in the dataset. Rather than treating all data uniformly, the variable reduction module selectively enhances the quality and relevance of specific variables by identifying and retaining only those with high predictive value for client behavior events, thus improving model accuracy without requiring all raw data
2Ease of operation
If model validation is performed manually, then flexibility is high, but productivity is low
Solution Approach 1:
The system enables models to validate themselves through automated validation modules that continuously assess model performance and accuracy. The model validation module automatically scores models based on predefined criteria and thresholds, eliminating the need for manual validation while maintaining flexibility through configurable validation parameters and thresholds that can be adjusted without programming changes
3Loss of information
If comprehensive model data is provided to all users, then information completeness is high, but system complexity increases
Solution Approach 1:
The system provides different levels of data completeness to different user roles based on their needs and permissions. The model market module implements role-based data delivery, where developers receive comprehensive model data for creation and validation, while end users receive only the specific model outputs and predictions relevant to their needs, thereby reducing system complexity while maintaining information completeness for each user category
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.