Credit Score Visualization Interface with High Scorer Comparison
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Solution Overview
Problem
Consumers face difficulties in understanding how their specific credit data affects their credit scores due to the complexity of paper credit reports and lack of personalized advice, making it hard to develop effective strategies to improve their credit scores.
Innovation Solution
A mobile application and computer-implemented method that provides a user interface to visualize and compare consumer-specific credit data with high scorers' data, allowing users to identify areas for improvement by categorizing credit information and explaining how each category impacts their credit score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If consumers use traditional paper credit reports to analyze their credit data, then they can obtain comprehensive credit information, but the complexity of the reports makes it difficult for consumers to understand how specific data affects their credit scores
Solution Approach 1:
The patent segments the complex credit report into distinct categories (payment history, credit utilization, length of credit history, etc.) and further divides each category into specific data elements. This segmentation allows consumers to understand the impact of individual credit data points on their overall score by presenting information in manageable, organized sections rather than as a monolithic document.
Solution Approach 2:
The patent introduces an intermediary processing system that acts as a mediator between the raw credit data and the consumer. This system automatically analyzes credit data, calculates category scores, determines the impact of specific data elements on the overall credit score, and presents this processed information in an easily understandable format, eliminating the need for consumers to manually interpret complex credit report structures.
2Adaptability or versatility
If generic credit improvement advice is provided to all consumers, then broad guidance can be given, but personalized strategies specific to individual consumer situations cannot be developed
Solution Approach 1:
The patent applies local quality by providing customized credit improvement advice tailored to each consumer's specific credit profile. Instead of giving uniform guidance to all consumers, the system analyzes individual credit data across multiple categories and generates personalized recommendations that address each consumer's unique strengths and weaknesses, ensuring the advice is locally optimized for their specific situation.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting credit improvement strategies based on individual consumer parameters such as current credit score, credit history length, debt levels, and payment patterns. The system processes these varying parameters to generate adaptive recommendations that change according to each consumer's specific data profile, enabling personalized guidance without requiring manual analysis of each case.
3Productivity
If consumers manually analyze their credit data to understand credit score calculation, then they can gain insights, but the process is time-consuming and complex
Solution Approach 1:
The patent implements self-service by enabling the credit analysis system to automatically perform the complex task of analyzing credit data and explaining score impacts without requiring consumer intervention. The system autonomously processes credit information, calculates category scores, identifies data elements affecting the credit score, and generates personalized improvement strategies, allowing consumers to obtain professional-grade analysis instantly without manual effort.
Solution Approach 2:
The patent replaces the manual mechanical process of credit analysis with an automated computational system. Instead of consumers manually reviewing credit reports and attempting to understand score calculations, the system uses automated algorithms to process credit data, apply scoring models, and generate interpretations, substituting the labor-intensive manual process with efficient automated computation that delivers results instantly.
Data Source
AI summary
A computer system receives credit information relating to a consumer and a number of high scorers, and determines credit score factors associated with the consumer and the high scorers. The system may construct flippable score factor displays comprising consumer specific information specific to a credit category that may be reversed to display explanatory text regarding how that credit category affects their credit report. The score factor display may include a comparison between the consumer's scores and the high scorers' scores in a number of categories. Scores of high scorers may be periodically refreshed. Additionally, the group of high scorers may be limited according to a particular demographic, such as a geographic location, that may be selectable by a user.