Anonymous Loan Issuance via Non-Identification Attribute Extraction
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Solution Overview
Problem
Existing mobile banking systems require extensive personal data for creditworthiness determination and loan processing, leading to inefficiencies and increased complexity, especially for smaller nano and micro-loans.
Innovation Solution
A method and system that connects a mobile device to a loan issuance server via a wireless network, using non-identification attributes from an electronic wallet and public data sources to generate a credit score and approve loans anonymously, employing machine learning models to predict bad debt risk and adjust credit limits, without requiring full names, credit card numbers, or government IDs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive personal data is collected for creditworthiness determination, then loan approval accuracy is improved, but processing time and system complexity increase
Solution Approach 1:
The patent extracts only the essential non-identification attributes needed for credit scoring from the full set of personal data. Instead of processing complete personal information including names, addresses, and social security numbers, the system selectively retrieves only demographic and financial attributes required for creditworthiness assessment, thereby reducing processing time while maintaining accuracy.
Solution Approach 2:
The system performs preliminary data preparation and attribute extraction before the actual credit scoring process. User profiles are pre-configured with relevant attributes, and the system retrieves only necessary data elements in advance, avoiding time-consuming data collection and processing during the loan approval moment.
2Measurement precision
If extensive personal data is collected for creditworthiness determination, then loan approval accuracy is improved, but data processing overhead and system complexity increase
Solution Approach 1:
The patent extracts only the essential non-identification attributes needed for credit scoring from the full set of personal data. Instead of processing complete personal information including names, addresses, and social security numbers, the system selectively retrieves only demographic and financial attributes required for creditworthiness assessment, thereby reducing processing time while maintaining accuracy.
Solution Approach 2:
The system introduces an intermediary layer of attribute-based user profiles that mediate between the database and the credit scoring engine. Instead of directly processing personal data, the system uses standardized attribute strings as intermediaries, simplifying data flow and reducing system complexity while maintaining the necessary information for accurate credit assessment.
3Reliability
If personal identification data is required for loan processing, then credit history verification is improved, but user anonymity and processing efficiency decrease
Solution Approach 1:
The patent creates simplified copies of user identities using randomly generated user ID numbers and attribute strings instead of requiring original personal identification data. These synthetic identity representations contain sufficient information for credit history verification while enabling faster processing and protecting user privacy. The system matches attributes to existing user profiles using these copied identifiers rather than processing full personal data.
Data Source
AI summary
A system and method determines the creditworthiness of a consumer and issues a loan and generates a behavioral profile for that consumer. An initial set of data is acquired from the consumer that includes non-identification attributes without obtaining a full name, a credit card number, a passport number, or a government issued ID number that allows identification of the consumer. A user ID number matches the initial set of data to a physical user in a transaction database. A credit score based on the average credit among a plurality of user profiles is matched to determine a maximum credit for the consumer. A machine learning model may be applied to stored consumer loan data to determine when the consumer requires an increase in the maximum allowed credit and the risk involved with increasing the maximum allowed credit.