Credit Score Prediction Model Using Behavioral Pattern Extraction
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Solution Overview
Problem
Individuals lack clear guidance on achieving a good credit score due to proprietary credit score calculation methods, making it difficult for them to understand which financial behaviors lead to score improvements.
Innovation Solution
A system and method that analyze historical spending and payment data to generate models predicting credit score changes, allowing for personalized behavioral recommendations to enhance credit scores, using statistical and regression analyses to identify effective behaviors and refine models based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If credit score calculation methods are kept proprietary, then lenders can maintain competitive advantage and control risk assessment, but consumers cannot understand which behaviors lead to score improvements
Solution Approach 1:
The patent introduces an intermediary system that translates proprietary credit score calculations into understandable behavior patterns. The system acts as a mediator between the black-box credit scoring algorithms and consumers, extracting and presenting actionable behavior insights without exposing the underlying proprietary calculation methods. This allows consumers to understand and improve their credit scores while lenders maintain their competitive advantage.
2Measurement precision
If detailed spending and behavioral data are collected to provide personalized credit advice, then accuracy of credit score predictions improves, but privacy concerns and data security risks increase
Solution Approach 1:
The patent extracts only the necessary behavior patterns from detailed spending data without collecting or storing the raw personal financial information. The system processes data to identify actionable behavior insights while leaving the detailed personal data behind, thereby improving prediction accuracy while minimizing privacy and security risks associated with data collection and storage.
3Reliability
If complex statistical models are used to analyze behavioral patterns, then the ability to predict credit score changes improves, but the complexity of the system increases
Solution Approach 1:
The patent uses behavior patterns as an intermediary layer between complex statistical models and end-users. The complex regression analyses and statistical models are hidden within the system, while only simplified, actionable behavior recommendations are presented to consumers. This maintains high prediction reliability while reducing the perceived complexity for users.
Data Source
AI summary
System and methods for extracting behaviors and suggesting behaviors to achieve a desired credit score may include receiving, via a network, account data including information regarding currency outlays made by a first sample population; generating, by a computing device, a plurality of behavior patterns based on currency outlay patterns extracted from the account data; receiving, by the computing device, credit score data for the first sample population; formulating, by the computing device, a model for predicting a credit score change; and storing the model on a data storage device. The model may include variables corresponding to each of the plurality of behavior patterns and a likely credit score affect.