Dynamic Payment Pre-qualification via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack an efficient and dynamic method for automatically generating pre-qualification determinations for secured and unsecured payment instruments based on user-specific information and credit evaluations, failing to provide personalized and real-time offers and incentives.
Innovation Solution
A computer-implemented method using a machine learning algorithm trained with user data and historical payment instrument information to identify pre-qualified payment instruments, offering personalized options and updating based on user selections and credit performance, with the ability to perform soft credit inquiries and provide real-time tracking and incentives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual pre-qualification processes are used, then accuracy of credit evaluation may be maintained, but processing time and operational efficiency deteriorate
Solution Approach 1:
The patent replaces manual credit evaluation processes with an automated machine learning system that uses algorithms to analyze user data, credit history, and financial information. This substitution of mechanical/manual operations with automated computational systems directly increases processing speed while managing system complexity through modular architecture.
Solution Approach 2:
The system enables self-service pre-qualification determinations where users can immediately receive pre-qualification results and personalized offers without manual intervention. The automated ML model independently evaluates creditworthiness and generates offers, eliminating the need for human reviewers and significantly improving processing efficiency.
2Adaptability or versatility
If personalized pre-qualification offers are generated using machine learning, then user experience and customization improve, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary credit evaluations and generates pre-qualification offers before users formally apply for payment instruments. The ML model pre-processes user data and generates personalized offers in advance, reducing the computational burden during formal application processing and enabling rapid personalization without excessive resource consumption.
Solution Approach 2:
The patent dynamically adjusts evaluation parameters and offer terms based on user-specific data, credit history, and real-time financial conditions. The ML model modifies offer parameters such as credit limits, interest rates, and security deposit requirements to personalize each offer while optimizing computational efficiency through parameter-based decision-making rather than full re-evaluation.
3Speed
If real-time credit evaluation and offer generation are implemented, then responsiveness and user satisfaction improve, but system complexity and computational requirements worsen
Solution Approach 1:
The patent divides the credit evaluation system into modular components: data collection modules, ML model evaluation modules, offer generation modules, and decision-making modules. This segmentation enables real-time processing by allowing each module to operate independently and concurrently, improving response time while managing system complexity through modular design that facilitates maintenance and scaling.
Data Source
AI summary
Systems and methods for automatic and dynamic pre-qualification determinations associated with secured and unsecured payment instruments are provided. The systems and methods are used to automatically generate and provide pre-qualification determinations for different payment instruments based on various factors. The systems and methods further provide these pre-qualification determinations along with any applicable terms and conditions corresponding to the payment instruments a user is pre-qualified for.