Real-Time Approval Logic Alignment Using ML Confidence Feedback
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Solution Overview
Problem
Conventional systems fail to leverage machine learning models to synchronize the logic of batch-based resource processing systems with real-time systems, leading to inefficiencies and inaccuracies due to data format differences and the inability to directly train real-time systems using machine learning techniques.
Innovation Solution
A machine learning model is trained using a dataset from a rule set-based batch processing system to correlate resource access requests with approval determinations, generating confidence scores for real-time system decisions and adjusting logical operations based on discrepancies and explainability vectors to align with batch-based processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a real-time resource processing system is built without leveraging batch-based system logic, then the system can be designed with modern real-time processing capabilities, but the system will lack the proven logic and accuracy of the legacy batch-based system
Solution Approach 1:
A machine learning model is introduced as an intermediary component that bridges the legacy batch-based approval system and the new real-time processing system. The ML model is trained on historical data from the batch system to learn its decision logic, then deployed to generate predictions for real-time requests. These predictions are used to guide and validate the real-time system's approval decisions, ensuring consistency with the proven batch system logic while enabling real-time processing speeds.
2Ease of manufacture
If machine learning techniques are applied directly to train real-time systems, then training efficiency can be improved, but data format differences and system constraints prevent direct application
Solution Approach 1:
Instead of training the real-time system directly using complex machine learning techniques, the invention creates a simplified copy of the batch system's decision logic through a machine learning model trained on historical batch data. This ML model captures the essential approval logic in a format that is easier to train and deploy. The model processes real-time requests and outputs predictions that guide the real-time system's decisions, effectively copying the batch system's intelligence without requiring direct training of the real-time system using complex ML methods.
3Speed
If the real-time system operates independently from the batch-based system, then processing speed increases, but output variance and logic divergence occur
Solution Approach 1:
The machine learning model serves as a feedback mechanism that continuously monitors and guides the real-time system's approval decisions. The model is trained on historical batch system data, establishing the expected logic and decision patterns. When the real-time system processes requests, the ML model provides predictions and confidence scores that feedback into the decision-making process. This feedback loop ensures that the real-time system maintains logic consistency with the batch system while operating at real-time processing speeds, preventing output variance and logic divergence.
Data Source
AI summary
Systems and methods for using a machine learning model for alignment with a rule set. In some aspects, the system receives a training dataset. The system trains a machine learning model based on the training dataset. The system processes, using a real-time resource processing system, a first resource access request, a first initial account state, and a first subsequent account state from a real-time data stream and generates a first approval determination. The system applies the machine learning model to the real-time data stream to generate a first confidence score that the new resource access request was executed correctly. Based on the first confidence score, the system determines that there is a discrepancy between the first approval determination by the real-time resource processing system and an expected approval of the machine learning model. Based on the discrepancy and a runtime explainability vector, the system adjusts the real-time resource processing system.