Credit Decision Platform Using Email History for Thin-File Applicants
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Individuals with insufficient domestic credit history face challenges in obtaining credit products due to lack of reliable credit data, as traditional methods rely on domestic credit reporting agencies which may not have sufficient or portable credit information, especially for expatriates or those with non-traditional credit sources.
Innovation Solution
A credit decision platform that accesses and analyzes email accounts to generate non-domestic historical data using machine learning models and natural language processing, determining creditworthiness by identifying relevant financial history metrics from email content, thereby supplementing insufficient domestic credit data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional credit evaluation methods using domestic credit reporting agencies are used, then credit decisions can be made for applicants with sufficient domestic credit history, but applicants with insufficient domestic credit history cannot be evaluated accurately
Solution Approach 1:
The patent uses email accounts as an intermediary data source to bridge the gap for applicants with insufficient domestic credit history. Email data serves as a mediator that contains indirect evidence of financial behavior, payment habits, and creditworthiness when traditional credit bureau data is unavailable. The system extracts financial information from email communications to evaluate applicants who lack conventional credit records.
Solution Approach 2:
The patent transitions from evaluating creditworthiness solely based on domestic credit bureau data to incorporating email communication data as an additional dimension. This dimensional shift allows the system to assess applicants with insufficient credit history by analyzing patterns in email content, such as financial transactions, payment confirmations, and credit-related communications, thereby expanding the evaluation framework beyond traditional sources.
2Reliability
If domestic credit data is used for evaluation, then credit decisions are reliable for domestic applicants, but the data is not portable for expatriates or international applicants
Solution Approach 1:
The patent implements a universal credit evaluation approach that can handle both domestic and international applicants through a single system. By incorporating email data analysis, the system achieves multi-functionality: it evaluates traditional credit bureau data for domestic applicants while simultaneously analyzing email communications for international applicants with insufficient domestic credit history, making the credit decision process universally applicable across different nationalities and residency statuses.
Solution Approach 2:
Email data serves as a portable intermediary that transcends national borders and credit bureau limitations. Unlike domestic credit data that is confined to specific countries' credit reporting systems, email communications provide a universal data source that follows applicants regardless of their location or residency status, enabling credit evaluation for expatriates and international applicants who lack local credit history.
3Adaptability or versatility
If email data analysis is implemented, then creditworthiness can be estimated for applicants with insufficient credit history, but system complexity increases
Solution Approach 1:
The patent replaces manual credit evaluation processes with automated machine learning models and natural language processing systems. Instead of requiring human analysts to manually review email communications for creditworthiness assessment, the system uses automated algorithms to extract, analyze, and interpret financial information from email data, significantly reducing operational complexity while maintaining evaluation capability for applicants with insufficient credit history.
Solution Approach 2:
The system implements self-service functionality where the credit evaluation process automatically accesses and analyzes email data without requiring additional manual intervention. The machine learning models autonomously extract relevant financial information, pattern recognition occurs automatically, and creditworthiness estimates are generated without human assistance, reducing the perceived complexity for users while enabling comprehensive evaluation of applicants with limited credit history.
Data Source
AI summary
In some implementations, a credit decision platform may receive a credit request from an applicant and obtain domestic historical data associated with the applicant from a credit bureau device. The credit decision platform may obtain access to an email account associated with the applicant based on determining that the domestic historical data associated with the applicant is insufficient to process the credit request. The credit decision platform may identify, using one or more machine learning models, a set of email messages included in the email account that are relevant to the credit request and may analyze content included in the set of email messages to generate non-domestic historical data associated with the applicant. The credit decision platform may generate a decision on the credit request based on an estimated creditworthiness of the applicant, which may be determined based on the non-domestic historical data.