Secure Loan Dataset Platform for Retirement Plan Default Monitoring
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Solution Overview
Problem
Current systems for managing retirement plan loans face inefficiencies due to the inability to seamlessly communicate and transfer data between different computing systems, leading to delayed and inaccurate identification of loan defaults and recoupment of unpaid loans, which hinders employers' efforts to manage fiduciary duties and provide timely financial information to participants.
Innovation Solution
A server-based system that communicates with multiple computing environments to create uniform, data-format-agnostic datasets, monitors employment status changes, and generates transactions, while also providing a platform for real-time data visualization and predictive modeling to simulate scenarios and notify users of potential losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification systems are used to identify loan defaults, then human reviewers can determine employment status changes, but the process becomes tedious, time-consuming, and results in delayed and inaccurate results
Solution Approach 1:
The patent replaces manual verification systems with automated computer-implemented systems that use web crawling, data mapping, and predictive modeling to identify employment status changes and loan defaults. This substitution eliminates human reviewers from the process, enabling real-time monitoring and accurate identification of defaults without time delays.
Solution Approach 2:
The system enables self-service by allowing the automated platform to independently monitor employment status, identify defaults, and notify relevant parties without requiring human intervention. The web crawling and data analysis functions operate autonomously to detect changes and trigger appropriate responses.
2Adaptability or versatility
If different computing systems use different data formats and ecosystems, then each system can maintain its own standards, but the systems cannot effectively communicate or transfer loan datasets between them
Solution Approach 1:
The patent introduces a standardized data format and mapping layer that acts as an intermediary between different computing systems. This intermediary enables seamless communication and data transfer between systems with different data ecosystems by translating and harmonizing data formats without losing information.
Solution Approach 2:
The system creates a universal data format that can be used across multiple computing systems and platforms. This universal format enables any system to send, receive, and process loan datasets regardless of their native data ecosystem, achieving full interoperability.
3Speed
If real-time monitoring of employment status is implemented, then defaults can be identified immediately, but the system requires continuous data collection and processing from multiple sources
Solution Approach 1:
The patent replaces complex manual monitoring processes with automated web crawling and data collection systems that continuously scrape employment data from multiple online sources. This automation handles the complexity of continuous data collection from various sources while providing real-time identification of employment status changes.
Solution Approach 2:
The system implements continuous monitoring by constantly collecting and analyzing employment data from multiple sources without interruption. This continuous action ensures that any employment status changes are detected immediately, enabling real-time identification of defaults.
4Loss of information
If predictive modeling is used to simulate default scenarios, then users can be notified of potential losses, but the system requires processing power to execute models and generate scenarios
Solution Approach 1:
The system uses predictive modeling selectively to generate scenarios only when needed, such as when employment status changes are detected or at scheduled intervals. This partial action approach provides sufficient information about potential losses without continuously executing computationally intensive models, optimizing resource usage.
Data Source
AI summary
Embodiments herein recite a method including receiving a request to create a secure loan dataset, retrieving one or more data records associated with a user profile and a secure loan dataset associated with a first user, the one or more data records comprising at least a triggering employment status attribute that causes the server to execute a financial transaction associated with the secure loan dataset; mapping one or more data records associated with the user profile and the secure loan dataset; monitoring data associated with a modification to the triggering employment status attribute of a plurality of users of an enterprise; training a predictive model using the data associated with the plurality of users; executing the predictive model to predict data associated with the triggering employment status attribute; and generating a notification that includes a likelihood of the triggering employment status attribute.