ML Response Attribute Selection for Consistent Audit Data Requests
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Solution Overview
Problem
Current methods for responding to data requests in compliance audits are time-consuming, inconsistent, and fail to consider historical responses, leading to inaccurate or inconsistent reporting due to the increasing complexity of regulations and data formats.
Innovation Solution
A system utilizing machine learning models trained on historical attribute metrics to generate and validate response attributes, selecting the model with the greatest validation metric to ensure accurate and efficient responses to data requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual methods are used to respond to data requests in compliance audits, then human reviewers can evaluate and verify each response, but the process becomes time-consuming and inconsistent
Solution Approach 1:
The system performs preliminary actions by training multiple machine learning models on historical audit data and response patterns before actual audits occur. These pre-trained models are then ready to rapidly generate consistent responses during the audit process, eliminating the need for time-consuming manual evaluation while maintaining reliability through pre-established validation metrics.
Solution Approach 2:
The patent replaces the mechanical system of manual human review with an automated machine learning-based system. Multiple ML models generate responses and validation metrics automatically, substituting human time and effort with computational processes that provide consistent, repeatable results without the variability inherent in manual evaluation.
2Measurement precision
If multiple machine learning models are used to generate response attributes, then accuracy and validation can be improved, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where validation metrics generated by the ML models are used to automatically select the best-performing model for each data request. This feedback loop allows the system to manage multiple models efficiently by using their performance metrics to determine which model to deploy, thereby maintaining accuracy while controlling complexity through automated model selection rather than manual management.
Solution Approach 2:
The patent changes the parameter of model selection dynamically based on validation metrics. Instead of using a fixed single model, the system adjusts which model is active based on performance parameters derived from historical data, allowing it to adapt to different data request types and maintain high accuracy without permanently maintaining all model complexities simultaneously.
3Stability of the object's composition
If historical attribute metrics are used to train machine learning models, then response consistency improves, but data processing requirements increase
Solution Approach 1:
The system performs preliminary action by training ML models on historical attribute metrics before production use. This upfront training phase processes the large volume of historical data once, embedding the consistency patterns into the model structures. During actual audit responses, the models leverage this pre-processed knowledge without requiring real-time access to the full historical dataset, thus achieving consistency while reducing ongoing data processing requirements.
Data Source
AI summary
An embodiment includes generating, responsive to detecting a data request by a system, a response attribute by a machine learning model based on the data request wherein the machine learning model is trained on a historical attribute metric. The embodiment includes determining a validation metric corresponding to the machine learning model, wherein different machine learning models correspond to different validation metrics. The embodiment also includes deciding, by the system to modify the response attribute, by selecting the machine learning model with a greatest validation metric determined for the response attribute.