Decision Model Validation Against Regulatory Laws
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Industries face challenges in ensuring that their models and data comply with relevant laws, as existing methods lack effective validation processes to assess compliance with regulatory requirements.
Innovation Solution
A system that deconstructs decision models into branching decisions and compares them to a Markov chain generated from regulatory laws, using a sequence generation model to validate compliance, ensuring that the models align with legal standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If companies use models and data in business operations, then productivity and decision-making improve, but compliance with regulatory laws becomes difficult to ensure
Solution Approach 1:
The system performs preliminary validation of models and data against regulatory requirements before they are deployed for business decisions. The validation process proactively checks compliance issues beforehand, allowing companies to maintain high productivity while ensuring regulatory compliance is established prior to operation.
Solution Approach 2:
The system provides continuous feedback on model and data compliance status, comparing actual business operations against regulatory requirements. This feedback mechanism enables real-time compliance monitoring and adjustment, ensuring that productive business decisions remain within legal boundaries.
2Reliability
If comprehensive validation processes are implemented to ensure compliance, then regulatory reliability improves, but system complexity and validation time increase
Solution Approach 1:
The system introduces an intermediary validation layer that sits between business operations and regulatory requirements. This intermediary component automatically translates complex regulatory laws into actionable validation rules, simplifying the overall system architecture while maintaining comprehensive compliance checking capabilities.
Solution Approach 2:
The system creates a virtual copy or representation of regulatory requirements that can be systematically compared against business models and data. This copying approach allows comprehensive validation without requiring direct manipulation of complex legal texts, reducing system complexity while improving validation accuracy.
3Measurement precision
If detailed model deconstruction and comparison processes are used, then compliance measurement precision improves, but validation time and processing resources increase
Solution Approach 1:
The validation process is segmented into distinct modular components that can process different aspects of compliance independently. The system divides model deconstruction into separate validation steps, allowing parallel processing and reducing overall validation time while maintaining precise compliance measurement through systematic comparison of segmented components.
Data Source
AI summary
The present disclosure provides computing systems and techniques for validating a decision model against a cannon of regulation. A server can deconstruct a decision model into a number of branching decisions and also generate a Markov chain comprising a number of sequences from a cannon of regulation. The server can compare the branching decisions to the sequences and can validate the decision model with the cannon of regulation based on the comparison.