Machine Learning Model for Customized Credit Card Debt Reduction Plans
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Solution Overview
Problem
Existing credit card debt reduction strategies fail to consider individual consumers' demographic and financial attributes, leading to ineffective debt repayment plans due to high interest rates and varying financial situations.
Innovation Solution
A method and system utilizing machine learning models, specifically neural networks, to determine customized debt reduction plans by correlating users' financial and demographic attributes with those of successful debt payers, ranking plans based on likelihood of success, and adapting plans based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional debt reduction strategies are used, then debt repayment can be attempted, but the strategies fail to account for individual consumer attributes leading to low success rates
Solution Approach 1:
The patent applies local quality by customizing debt reduction plans according to specific local characteristics of each consumer, including their demographic attributes (age, income, family size) and financial attributes (debt amount, interest rate, cash flow). Instead of applying a uniform strategy, the system tailors the repayment plan to match the individual consumer's unique situation, thereby improving success rates while maintaining adaptability.
2Reliability
If machine learning models are used to customize debt reduction plans, then repayment success likelihood increases, but system complexity increases
Solution Approach 1:
The patent employs a machine learning model that serves multiple functions: it analyzes demographic attributes, evaluates financial attributes, compares consumer profiles against a database of successful repayments, and generates customized debt reduction plans. This multi-functional approach consolidates what could be separate complex systems into a single unified model, improving reliability while managing overall system complexity through functional integration.
3Adaptability or versatility
If demographic and financial attributes are collected and analyzed, then customized plans can be created, but data processing requirements and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-processing and storing demographic and financial attribute data in structured formats before actual plan generation. Consumer profiles are prepared in advance with normalized attributes, and the machine learning model is pre-trained on historical repayment data. This preliminary preparation reduces the computational burden during real-time plan customization, enabling adaptability while managing energy and resource consumption efficiently.
Data Source
AI summary
This disclosure relates to systems and methods for constructing a customized debt reduction plan for a user. In some implementations, a customized debt reduction system obtains a plurality of financial attributes of the user and a plurality of other users, where the plurality of financial attributes are indicative of credit card debt, and identifies users from the plurality of other users who successfully repaid their credit card debt based on their respective financial attributes and one or more repayment techniques that resulted in successful repayment of their credit card debt. The customized debt reduction system correlates the plurality of financial attributes of the user with the plurality of financial attributes of a number of the identified users and determines a personalized score for the user, using a trained machine learning model, based on the correlation to determine a customized debt reduction plan for the user based on the personalized score.