Enterprise Compliance Model Updating With Stakeholder Label Validation
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Solution Overview
Problem
Monitoring compliance within large enterprises is challenging due to the numerous regulatory and corporate compliance requirements, which can be difficult to track and enforce across various departments and subsidiaries, leading to potential non-compliant behaviors and associated risks.
Innovation Solution
A system and method for generating and updating a compliance model using stakeholder feedback, involving a generation module that includes a setup sub-module, a generative model sub-module, a label generation sub-module, and a validation sub-module to create and refine training data for a compliance model, and an update module to adapt to changes in compliance requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional compliance monitoring methods are used, then implementation is simple, but monitoring accuracy and effectiveness deteriorate due to the large number of compliance requirements
Solution Approach 1:
The patent segments the complex compliance monitoring task into multiple components: a generative model for data synthesis, a labeling module for annotation, and an iterative training system. This segmentation allows each component to handle specific aspects of compliance monitoring, improving overall accuracy while managing system complexity through modular design.
Solution Approach 2:
The patent introduces synthetic data as an intermediary element between compliance requirements and monitoring analysis. The generative model creates intermediate synthetic datasets that capture compliance patterns, which then serve as training material for the monitoring system, improving accuracy without requiring direct analysis of all raw compliance data.
2Reliability
If comprehensive compliance tracking is implemented, then monitoring coverage is improved, but difficulty in tracking and enforcement increases due to numerous requirements across departments
Solution Approach 1:
The patent implements an iterative feedback mechanism where the compliance monitoring system continuously evaluates its performance on synthetic data, identifies areas for improvement, and retrains the model accordingly. This feedback loop enhances monitoring reliability by systematically improving detection capabilities while managing complexity through automated evaluation and adjustment.
Solution Approach 2:
The patent performs preliminary actions by generating synthetic compliance data and training the monitoring system before actual compliance assessment. This pre-training on synthetic datasets prepares the system to handle real compliance scenarios more effectively, reducing the difficulty of detecting and measuring compliance across multiple departments.
3Adaptability or versatility
If static compliance models are used, then system simplicity is maintained, but adaptability to changing regulatory requirements deteriorates
Solution Approach 1:
The patent transforms the compliance model from a static structure to a dynamic one through iterative training on newly generated synthetic data. The model continuously adapts to changing regulatory requirements by incorporating updated synthetic datasets, maintaining versatility while managing updating complexity through automated retraining processes.
Solution Approach 2:
The patent utilizes parameter changes in the synthetic data generation process to reflect evolving compliance requirements. By adjusting the parameters of the generative model to match new regulatory conditions and regenerating synthetic data accordingly, the system achieves adaptability while the automated parameter adjustment manages the complexity of model updates.
Data Source
AI summary
The present disclosure relates to a system and method for improved monitoring compliance within an enterprise based on inputs received from stakeholders. The present invention provides a system for generation of compliance model and updation of compliance model based in changes in regulatory compliances. The generation model is configured to generate a first set of labels and a first set of data points. Further, the first set of labels are validated by one or more said user to generate a second set of labels for additional data points based on validation of first set of labels. The generation of first and second set of label generates training ready data from the first and second set of labels for training a data model. Further, the present invention provides for updating of compliance model based on update in regulatory compliance rules. In updating the compliance model, the system provides generation of third set of labels corresponding to additional data points corresponding to new rules/compliance and the third set of labels are validated by users and the validated labels are used for re-training the compliance model.