Credit Scoring System Daily Data Update Architecture
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Solution Overview
Problem
Current credit scoring systems, such as those using FICO scores and First Data Resources Corporation's risk scores, primarily update on a monthly basis, which may not accurately reflect recent credit behavior, leading to potential defaults and affecting profit margins for credit card issuers.
Innovation Solution
A system and method for evaluating creditworthiness by daily and periodic updates from credit reporting organizations and account transaction data, combining historical and recent behavior data to generate a credit score ranging from 0 to 980, allowing for more accurate prediction of credit risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If credit scoring systems update on a monthly basis, then system complexity and data processing requirements are reduced, but the accuracy of predicting credit defaults deteriorates
Solution Approach 1:
The patent segments the credit scoring system into multiple independent data sources (credit reporting organizations, account transaction data, historical behavior data) that can be updated at different frequencies. This allows daily updates of critical data without requiring complete system reconstruction, thereby improving prediction accuracy while managing complexity through modular architecture.
Solution Approach 2:
The system dynamically adjusts data update frequencies based on the importance and volatility of different data types. Critical data such as account transaction data and significant events are updated daily, while less volatile data like historical credit reports are updated periodically. This dynamic approach optimizes prediction accuracy without uniformly increasing system complexity across all data streams.
2Measurement precision
If daily updates of credit information are implemented, then the accuracy of credit risk assessment is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent extracts and prioritizes only the most critical data elements for daily processing, such as significant events, new delinquencies, and recent account transactions. Less critical historical data is updated periodically rather than daily. This extraction approach maintains high credit risk assessment accuracy while significantly reducing the volume of data requiring daily processing.
Solution Approach 2:
The system applies partial action by updating only the necessary subset of credit data daily rather than complete credit files. Critical indicators are refreshed daily to maintain assessment accuracy, while comprehensive credit reports are updated less frequently, optimizing the balance between processing time and assessment precision.
3Reliability
If multiple data sources are integrated for credit scoring, then the predictive capability is improved, but system complexity and integration requirements increase
Solution Approach 1:
The patent implements a universal data integration architecture that handles multiple data sources (credit reporting organizations, account transaction systems, historical behavior databases) through standardized interfaces and common processing logic. This multi-functional framework enables the system to integrate diverse data types while maintaining manageable complexity through consistent data handling procedures across all sources.
Solution Approach 2:
The system introduces intermediary data processing layers that standardize and normalize data from different sources before integration. These intermediaries translate various data formats and structures into a unified schema, facilitating seamless integration of multiple data sources while reducing the complexity of direct point-to-point connections between systems.
4Loss of information
If historical and recent behavior data are both incorporated, then the comprehensiveness of credit evaluation is improved, but data management complexity increases
Solution Approach 1:
The patent organizes historical and recent behavior data along temporal dimensions, with recent data (daily updates) separated from historical data (periodic updates). This dimensional organization allows the system to maintain comprehensive credit evaluation by preserving both timeframes while simplifying data management through time-based segmentation and differentiated update strategies.
Data Source
AI summary
According to one embodiment, the invention relates to a system and method for evaluating the creditworthiness of an account holder of a credit account comprising the steps of determining, at least once a day, whether a first data set relating to the creditworthiness of the account holder has been received from a credit reporting organization; determining, at least once a day, whether a second data set relating to transaction activity of the credit account has been received; periodically receiving from a credit reporting organization a third data set relating to the creditworthiness of the account holder; periodically receiving a fourth data set relating to the historical activity of the credit account; and using the first and second data sets, to the extent they have been received, and the third and fourth data sets to determine a measure of creditworthiness.