Machine Learning Credit Recommendation System

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Consumers face challenges in improving their credit scores due to unclear guidance on how to effectively use credit building tools, leading to suboptimal usage and potential adverse financial impacts.

Innovation Solution

A credit building system utilizing machine learning models to provide personalized recommendations to users on activities that enhance their credit scores, integrating with digital channels to monitor user behavior and adjust strategies in real-time, while also offering rewards for meeting credit goals.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If consumers use credit building tools without clear guidance, then they can access credit tools, but their usage becomes suboptimal and may have adverse financial impacts

Engineering Contradiction:

Improveease of using credit building toolsVSAvoideffectiveness of credit score improvement

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system continuously monitors user activity and provides real-time feedback through personalized recommendations and notifications. This feedback loop guides consumers on optimal credit building behaviors, transforming unclear usage into informed decision-making while maintaining ease of operation through automated guidance

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The automated recommendation system enables consumers to self-serve their credit building needs without requiring manual intervention from financial institutions. The system autonomously analyzes user behavior, generates personalized recommendations, and monitors progress, allowing consumers to take control of their credit health independently

Inventive Principle:

Principle #25Self-service

2Reliability

If financial institutions provide manual guidance for credit building, then consumers receive clear instructions, but the workload for financial institutions increases

Engineering Contradiction:

Improveeffectiveness of credit score improvementVSAvoidworkload for financial institutions

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system automates the guidance provision process, allowing the financial institution's platform to self-serve by autonomously analyzing user data, generating recommendations, and monitoring outcomes. This eliminates the need for manual intervention while maintaining high effectiveness in credit score improvement

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes (human advisors providing guidance) with automated digital systems using machine learning algorithms. This substitution maintains the reliability of personalized guidance while significantly reducing the operational workload on financial institutions

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If consumers are provided with detailed credit building guidance, then they can improve their credit scores effectively, but the complexity of understanding and implementing the guidance increases

Engineering Contradiction:

Improveeffectiveness of credit score improvementVSAvoidcomplexity of guidance implementation

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system applies local quality by tailoring guidance specifics to each user's unique situation. Rather than providing generic complex information, it delivers personalized recommendations based on individual credit history, goals, and behavior patterns, making the guidance both effective and easier to understand and implement

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system dynamically adjusts guidance parameters based on real-time user activity and progress. It modifies recommendations, priorities, and messaging based on what works for each user, transforming static complex guidance into adaptive, simplified instructions that evolve with the user's credit building journey

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20240346579A1Generating credit building recommendations through machine learning analysis of user activity-based feedback

Publication Date: 2024.10.17 CHIME FINANCIAL INC

AI summary

A real-time activity recommendation system receives an input from a user device regarding a targeted financial goal, such as a target credit score. Using machine learning models to evaluate patterns of user activity that contribute positively towards the goal, and to evaluate the limitations and opportunities of the user's financial circumstances and profile, the recommendation system makes an assessment in real time to determine user actions that can be taken to improve credit health based on a user's profile and activity data. A user-specific recommendation regarding an activity that should be performed to reach the goal is generated and transmitted to the user. User and third party activity is later monitored as the user's financial status changes over time, and the recommendations are updated accordingly.