Automated Multi-Product Qualification With Real-Time Machine Learning
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Solution Overview
Problem
Existing qualification systems for multiple products operate separately, requiring users to submit the same information multiple times and lacking an efficient method to determine eligibility for multiple products simultaneously.
Innovation Solution
A system utilizing a trained machine learning model dynamically analyzes user information and product qualification criteria in real-time to identify a subset of products the user qualifies for, providing recommendations and continuously updating the model with new data for future decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate qualification systems are used for each product, then each product can have its own specific qualification criteria, but users must submit information multiple times and the process becomes inefficient
Solution Approach 1:
The patent combines multiple separate qualification systems into a single unified qualification system that can evaluate multiple products simultaneously. The system receives user information once and uses machine learning models to determine eligibility for multiple products across different categories (e.g., credit cards, loans, mortgages) in a single evaluation process, eliminating the need for users to submit information multiple times to different separate systems.
Solution Approach 2:
The unified qualification system is designed to handle multiple product types and qualification criteria through a single multi-functional platform. The system can evaluate users for various financial products including credit cards, loans, mortgages, and investment products, all through one qualification interface, making the system universally applicable across different product lines while maintaining product-specific evaluation capabilities.
2Productivity
If a unified qualification system is implemented, then processing efficiency improves and multiple submissions are eliminated, but the system complexity increases
Solution Approach 1:
The unified qualification system is segmented into distinct functional modules including information reception modules, machine learning model evaluation modules, and result generation modules. Each module handles specific tasks independently - receiving user information, evaluating against different product criteria using appropriate ML models, and generating qualification results - which reduces overall system complexity while maintaining high processing efficiency.
Solution Approach 2:
The system introduces machine learning models as intermediary components between the unified qualification interface and the various product-specific criteria. These ML models act as mediators that translate user information into qualification decisions for different product types, simplifying the architecture by replacing complex rule-based evaluation systems with adaptive learning-based intermediaries.
3Device complexity
If traditional rule-based qualification systems are used, then the logic is simple and transparent, but they cannot adapt to changing qualification criteria over time
Solution Approach 1:
The qualification system transitions from static rule-based logic to dynamic machine learning models that can adapt to changing qualification criteria. The ML models are trained on historical data and can learn new patterns and requirements as qualification criteria evolve over time, allowing the system to dynamically adjust its evaluation approach without requiring manual rule updates for each change.
Solution Approach 2:
The system changes the fundamental parameter of qualification logic from fixed rules to adaptive model parameters. Machine learning models use learnable parameters that are optimized through training data, allowing the qualification criteria to be embedded as learned patterns rather than explicit rules. This enables the system to adapt to changing criteria by updating model parameters through continuous learning from new data.
Data Source
AI summary
Qualification decisioning systems and techniques are described. For instance, a system receives user information that is indicative of a user's income stream and/or asset(s). The system receives product qualification criteria data corresponding to products, with different products corresponding to different product-specific qualification criteria. The product qualification criteria data can change over time. The system dynamically analyzes the user information and the product qualification criteria data using a trained machine learning (ML) model in real-time as the user information and the product qualification criteria data continue to be received. The trained ML model identifies a subset of the plurality of products that the user qualifies for at a specific time. The system outputs recommendations for the subset of the plurality of products. The system dynamically trains the trained ML model further, using the recommendations and the user information as training data, to update the trained ML model for future qualification decisions.