API for Consumer-Permissioned Academic Data Credit Scoring
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Solution Overview
Problem
Traditional credit scoring models are inadequate for individuals with little to no credit history, and they fail to effectively utilize alternative consumer data that is more accessible for a larger population.
Innovation Solution
A networked data processing system that provides an API for consumer-permissioned data, enabling the integration of academic, employment, and income data into credit scoring and other applications, while handling data authentication, user permissions, and data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional credit scoring models are used, then credit assessment is simple and straightforward, but they are inadequate for individuals with little to no credit history
Solution Approach 1:
The patent changes the parameters used in credit scoring from traditional credit bureau data to alternative data sources including academic data (GPA, enrollment status), employment data, and income data. This allows the system to assess creditworthiness of individuals with little or no traditional credit history by using different measurement parameters that are more universally available.
Solution Approach 2:
The data processing system is designed to handle multiple types of data sources (academic, employment, income, and traditional credit data) through a unified API framework. This multi-functional approach enables the system to serve diverse populations including students, young professionals, and traditional consumers, making the credit assessment system universally applicable across different demographic groups.
2Adaptability or versatility
If alternative consumer data is utilized, then coverage of scorable consumers increases, but data complexity and processing requirements increase
Solution Approach 1:
The patent introduces a data processing system with API infrastructure that acts as an intermediary between various data sources (academic institutions, employers, banks) and credit scoring applications. This intermediary layer handles data authentication, permission management, extraction, normalization, and verification, thereby managing the complexity of alternative data sources while enabling broad population coverage.
Solution Approach 2:
The system segments the complex data processing task into distinct modular components: data source authentication, user permission verification, data extraction, data normalization, data verification, and score generation. Each component handles a specific aspect of the data pipeline, making the overall system more manageable and scalable while supporting diverse data sources.
3Adaptability or versatility
If academic data is integrated into credit scoring, then credit opportunities for young borrowers improve, but data normalization across different institutions becomes challenging
Solution Approach 1:
The system transforms academic data from various institutions into a standardized parameter set including GPA (on a 4.0 scale), enrollment status (full-time, part-time, graduated), and academic standing. This parameter standardization allows consistent credit scoring across different academic institutions while preserving the value of diverse academic records for young borrowers.
Solution Approach 2:
The data processing system serves as an intermediary that interfaces with diverse academic institution data systems through standardized APIs. It handles the complexity of data normalization by implementing transformation logic that converts institution-specific academic data formats into unified parameters usable for credit scoring, thereby enabling broad academic data integration without requiring each institution to implement custom solutions.
Data Source
AI summary
A networked data processing system that provides an application programming interface (API) for consumer-permissioned data, such as academic data, employment data and income data. In some implementations, the data processing system enables application developers to integrate consumer-permissioned data (such as academic data) into their applications. In some implementations, the API enables a developer to focus on other aspects of a given application, while leveraging the data processing system to handle aspects of gathering and processing the source data, such as authenticating the provenance of the source data, handling user permissions, extracting the source data, reviewing the source data, verifying the source data, generating one or more scores from the source data, analyzing the source data relative to a defined purpose, providing the information sought from the analyzed data, and the like.