Independent Model Validation Platform for Benchmark Risk Assessment
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Solution Overview
Problem
Conventional model validation systems rely on the development team to validate models, which can lead to undetected errors that may cause platform-wide failures, as these systems are not equipped to handle the complexity of model validation processes involving unique algorithms, data sets, and techniques.
Innovation Solution
A model validation platform that operates independently of the development environment, using a benchmark model to compare and validate models, providing comprehensive risk assessments and recommendations through a user interface, and accommodating complex algorithms and data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the development team validates models themselves, then validation process is simple and quick, but errors may go undetected and infect the entire platform
Solution Approach 1:
The patent introduces an independent model validation platform as an intermediary between model development and platform deployment. This separate validation system objectively assesses models without the bias of self-validation, detecting errors that would otherwise infect the entire platform while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The validation process is segmented into independent, modular components including data processing modules, model validation modules, and reporting modules. This segmentation allows the complex validation task to be broken down into manageable units that can be independently developed, maintained, and scaled without overwhelming system complexity.
2Adaptability or versatility
If conventional bug tracking tools are used for model validation, then workflow organization is simple, but complex model algorithms and data preparation techniques cannot be properly validated
Solution Approach 1:
The validation platform is designed as a universal system capable of validating diverse model types including machine learning models, statistical models, and simulation models. It handles various data preparation techniques and complex algorithms through a unified architecture that can adapt to different validation requirements without requiring separate systems for each model type.
Solution Approach 2:
The platform accepts and processes various model parameters and configurations through standardized interfaces. It can validate models with different algorithms, data sets, and preparation techniques by adjusting validation parameters and thresholds according to the specific model being validated, enabling versatile validation without proportional increase in system complexity.
3Reliability
If models are validated only after incorporation into the platform, then integration is straightforward, but platform-wide failures may occur due to undetected errors
Solution Approach 1:
The validation platform performs comprehensive model validation before models are incorporated into the larger platform. This preliminary validation action detects and prevents errors from infecting the entire platform, ensuring platform stability while the modular architecture enables efficient processing that minimizes validation delays.
Data Source
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AI summary
Methods and systems are described herein for integrating model development control systems and model validation platforms. For example, the methods and systems discussed herein recite the creation and use of a model validation platform. This platform operates outside of the environment of the independently validated models as well as the native platform into which the independently validated models may be incorporated. The model validation platform may itself include a model that systematically validates other independently validated models. The model validation platform may then provide users substantive analysis of a model and its performance through one or more user interface tools such as side-by-side comparisons, recommended adjustments, and/or a plurality of adjustable model attributes for use in validating an inputted model.