Dynamic Credit Value UI Generation via ML Models
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Solution Overview
Problem
Conventional systems face challenges in efficiently and accurately determining and displaying account-specific credit values and dynamic credit value conditions in real time, particularly on mobile devices, due to limitations in screen space, excessive navigation between user interfaces, and reliance on multiple third-party sources, which leads to inefficient use of computational resources and inaccurate risk assessments.
Innovation Solution
A dynamic modeling system utilizing a machine learning model and a credit value model to generate user interface elements that dynamically present account-specific credit values and conditions, by generating an activity score from user activity data and determining a dynamic credit value range and conditions, reducing the need for multiple interfaces and third-party sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional systems utilize multiple user interfaces to present account-specific values, then information completeness is improved, but device complexity and ease of operation deteriorate due to excessive navigation requirements
Solution Approach 1:
The patent combines multiple user interfaces into a single unified graphical user interface that displays account-specific values, credit limits, and transaction information simultaneously. This merging eliminates the need for excessive navigation between multiple interfaces while maintaining complete information presentation, directly resolving the contradiction between information completeness and ease of operation.
Solution Approach 2:
The graphical user interface is designed to perform multiple functions within a single interface: displaying account balances, credit limits, transaction histories, and allowing user interactions. This multi-functionality consolidates what previously required multiple specialized interfaces, improving ease of operation without sacrificing information completeness.
2Measurement precision
If conventional systems interface with multiple third-party sources to determine account-specific values, then measurement precision is improved, but use of energy and productivity deteriorate due to excessive computational resource consumption
Solution Approach 1:
The patent extracts the credit value determination functionality from external third-party sources and implements it directly within the system using an integrated credit value model. This extraction eliminates the need for continuous communication with multiple third-party APIs, significantly reducing computational resource usage, network bandwidth consumption, and processing time while maintaining accurate credit value determination through the integrated model.
Solution Approach 2:
The system performs credit value determination autonomously using its own credit value model and activity machine learning model, rather than relying on external third-party services. This self-service approach reduces dependency on external sources, minimizing network communication overhead and computational resources required for API calls and data synchronization.
3Device complexity
If conventional systems use rigid approaches to present information within limited screen spaces, then device complexity is reduced, but loss of information and ease of operation worsen due to inability to seamlessly present large amounts of information
Solution Approach 1:
The graphical user interface utilizes spatial arrangement and visual hierarchy to organize account-specific values and transaction information within the limited screen space of mobile devices. By strategically positioning elements and using visual cues, the interface presents comprehensive information without requiring complex navigation structures, maintaining system simplicity while avoiding information loss.
4Measurement precision
If conventional systems navigate between excessive number of user interfaces to present information correctly, then measurement precision is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The system pre-loads and displays all relevant account-specific information, including credit values, limits, and transaction data, within the single graphical user interface. This preliminary preparation eliminates the need for users to navigate through multiple interfaces to access different pieces of information, significantly reducing navigation time while maintaining accurate information presentation.
Data Source
AI summary
The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a machine learning model and a credit value model to generate user interface elements that present credit values and credit value conditions in real time for user accounts. For instance, the disclosed systems can generate an activity score using an activity machine learning model with internal user activity data of a user account. Then, utilizing a credit value model with the activity score and a user activity condition, the disclosed systems can determine a dynamic credit value range for the user account. Indeed, the disclosed systems can display user interface elements with selectable credit values from the dynamic credit value range. Additionally, the disclosed systems can utilize the credit value model to determine and display one or more dynamic credit value conditions for a selected credit value received from the selectable credit values.