Credit Offer Generation Using Multi-Source Transaction Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Financial-service providers face challenges in accurately assessing a business's creditworthiness due to incomplete transaction data, as businesses may conduct transactions through unrecorded platforms, leading to inaccurate credit offers and delays in loan provisioning.
Innovation Solution
A payment service system utilizes a risk model incorporating machine-learning and artificial intelligence to analyze transaction data from multiple sources, including third-party systems and non-transaction data, to provide proactive and accurate credit offers, ensuring comprehensive assessment of a merchant's repayment capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the financial-service provider uses only the business's transaction records available to them, then the credit assessment process is simple and fast, but the accuracy of the credit offer is reduced due to incomplete information
Solution Approach 1:
The patent introduces a data aggregator as an intermediary component that collects transaction data from multiple third-party platforms (e.g., e-commerce platforms, marketplaces) and consolidates it into a unified view. This mediator handles the complexity of interfacing with various external systems, allowing the credit assessment system to access comprehensive data without direct integration complexity with each platform
Solution Approach 2:
The credit assessment system is divided into modular components: a data collection module that gathers information from multiple sources, a data processing module that cleans and standardizes the data, and a credit scoring module that generates the final assessment. This segmentation allows each component to specialize in specific tasks and be developed/maintained independently
2Measurement precision
If the financial-service provider accesses transaction data from multiple third-party platforms, then the accuracy of credit assessment is improved, but the time required to provide credit offers increases
Solution Approach 1:
The system performs preliminary data collection and validation by setting up automated data pipelines that continuously gather transaction information from third-party platforms before credit assessment is needed. Historical transaction data is pre-processed and stored in a standardized format, so when a credit application is received, the assessment can proceed using already-prepared data rather than collecting it in real-time
Solution Approach 2:
The system dynamically adjusts data collection parameters based on the assessment stage: during initial screening, it uses aggregated summary metrics for quick evaluation, while for detailed credit offers, it retrieves comprehensive transaction histories. This parameter adjustment optimizes the balance between assessment accuracy and processing time
3Measurement precision
If the financial-service provider considers all information about the business, then comprehensive credit assessment is achieved, but false assessments may occur due to irrelevant information
Solution Approach 1:
The patent applies different levels of data processing and validation to different types of information sources. Transaction data from verified third-party platforms undergoes rigorous validation checks, while other business information receives appropriate but less intensive verification. Each data source is treated with the quality level matching its reliability, ensuring comprehensive assessment without being undermined by unverified information
4Measurement precision
If the financial-service provider collects comprehensive business data from various platforms, then accurate credit offers can be made, but the cost and complexity of data access increase
Solution Approach 1:
The patent implements a universal data interface layer that standardizes communication with multiple third-party platforms. This interface uses common protocols and data formats across different platforms (e-commerce, marketplaces, financial institutions), allowing the system to access diverse data sources through a single unified mechanism rather than requiring separate integration logic for each platform
Data Source
AI summary
In some examples, a system receives first transaction data for first transactions processed by a payment service, and uses a first model to determine, based on the first transaction data, that a probability of default for a user of the payment service satisfies a threshold. The system generates, based on a first financing factor, a first offer of credit for the user, and sends the first offer to the user. The system determines, based on third party information, that the user has additional income, and receives, from one or more of the third-party systems, second transaction data indicating the additional income. The system uses a machine learning model included in the first model to determine a second financing factor based on the second transaction data, and generates, based on the first financing factor and the second financing factor, a second offer of credit to send to the user.