Automated Account Balance Update via Risk Model Prioritization
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Solution Overview
Problem
Current user evaluation systems, such as those for microloan approvals, require extensive user interaction and information, which can be cumbersome, especially for users with limited access to the internet or brick-and-mortar stores, leading to inefficiencies in the evaluation process.
Innovation Solution
A computer-implemented method that streamlines user interaction by dynamically retrieving relevant user information, generating a priority level for requests, and using machine learning to assess and update account balances automatically, allowing for quick and accurate evaluations without additional user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive user interaction is required to provide information for evaluation, then the accuracy and completeness of user evaluation is improved, but the ease of operation and user convenience deteriorates
Solution Approach 1:
The system automatically retrieves user information from multiple sources without requiring user action. The cloud server autonomously collects data from payment processors, credit bureaus, and other third-party systems, allowing the evaluation process to serve itself rather than requiring user participation.
Solution Approach 2:
User information is pre-collected and stored in the cloud server from various sources before the evaluation is needed. Payment information, credit history, and other relevant data are gathered in advance, so when an evaluation is required, the information is already available for immediate processing.
2Reliability
If extensive user interaction is required for information provision, then the reliability of user evaluation is improved, but the loss of time in the evaluation process increases
Solution Approach 1:
All necessary user information is collected and verified in advance by the cloud server from multiple reliable sources. Credit reports, payment histories, and personal information are gathered before the evaluation trigger event occurs, eliminating delays during the actual evaluation process.
Solution Approach 2:
The cloud server continuously monitors and updates user information from connected systems. Data flows continuously from payment processors, credit bureaus, and other sources into the user profile, ensuring the information is always current and ready for immediate evaluation without interruption.
3Ease of operation
If manual processing of user requests is used, then the ease of operation is improved, but the productivity of the evaluation system deteriorates
Solution Approach 1:
The patent replaces manual mechanical processing with automated electronic systems. The cloud server automatically retrieves data, processes information, generates evaluations, and communicates results without human intervention, substituting electronic automation for manual operations throughout the entire evaluation workflow.
Solution Approach 2:
The evaluation system autonomously performs all processing tasks including data retrieval, analysis, decision-making, and result communication. The system serves itself by automatically managing the complete evaluation lifecycle without requiring manual operation, thereby maximizing productivity while maintaining operational simplicity through automation.
Data Source
AI summary
Embodiments disclosed are directed to a computing system that performs operations for streamlining user interaction in a user evaluation process. The computing system receives, at a first time, a request to update an account balance of a user account with a differential amount. The computing system determines a source of the request. The computing system generates a priority level for the request based on the determined source of the request. The computing system processes the request based on the generated priority level. The computing system generates a risk model associated with the user account based on electronic information associated with the user account. The computing system determines a supplemental amount associated with the user account based on the generated risk model. The computing system determines whether the differential amount is less than or equal to the supplemental amount. The computing system automatically updates the account balance of the user account, at a second time later than the first time, with the differential amount responsive to determining that the differential amount is less than or equal to the supplemental amount.