Merchant Classification via Payment Service Data Matching
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Solution Overview
Problem
Current merchant classification systems, such as those using merchant category codes (MCCs), often misclassify businesses, leading to higher fees and inaccurate tax reporting, which can result in financial inefficiencies and increased customer inquiries.
Innovation Solution
A system that utilizes a payment service to collect and compare merchant data, including reported, collected, and third-party data, to identify the most accurate business class by matching it with pre-defined business class profiles, allowing for reclassification to minimize fees and ensure correct tax reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional merchant category codes (MCCs) are used for classification, then the classification process is simple, but the classification accuracy is low leading to misclassification
Solution Approach 1:
The patent segments the classification process into multiple independent components: collecting merchant-reported data, gathering third-party data, obtaining collected data from payment transactions, comparing against business class profiles, and determining classification. Each component operates independently and contributes to the overall classification accuracy without requiring complete redesign of the entire system.
Solution Approach 2:
The payment service system performs multiple functions simultaneously: it processes payment transactions, collects merchant data, gathers third-party information, compares data against profiles, and determines business classification. This multi-functional approach improves classification accuracy without requiring a separate dedicated classification system, thereby managing complexity.
2Measurement precision
If multiple data sources are collected and compared to improve classification accuracy, then the precision of merchant classification improves, but the complexity of the classification system increases
Solution Approach 1:
The payment service acts as an intermediary that consolidates data from multiple sources (merchant-reported data, third-party data, and collected transaction data) and mediates the comparison process against business class profiles. This intermediary approach simplifies the overall system architecture by providing a single point of coordination rather than requiring direct integration between all data sources and classification logic.
Solution Approach 2:
Business class profiles are pre-defined and prepared in advance, containing the criteria and data patterns needed for classification. This preliminary preparation of classification standards allows the system to efficiently compare merchant data against established profiles without requiring complex real-time analysis frameworks, thereby managing processing complexity while maintaining high precision.
3Productivity
If manual merchant classification is performed, then the system complexity is low, but the productivity and accuracy of classification are reduced
Solution Approach 1:
The system enables self-service classification by automatically collecting merchant data, gathering third-party information, comparing against profiles, and determining classification without requiring manual intervention. The payment service autonomously performs the entire classification process, dramatically improving productivity while the modular architecture manages automation complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where classification results can be reviewed and adjusted, and where classification outcomes feed back into refining business class profiles. This feedback loop continuously improves classification accuracy and productivity while maintaining manageable automation levels through iterative optimization rather than requiring perfect initial automation.
4Reliability
If frequent reclassification is performed to maintain accuracy, then the classification remains up-to-date, but the processing time and computational resources increase
Solution Approach 1:
The system performs reclassification periodically at scheduled intervals rather than continuously, balancing the need for up-to-date classification with the consumption of processing time and resources. This periodic approach maintains classification reliability while avoiding the excessive computational burden of continuous real-time reclassification of all merchants.
Solution Approach 2:
The system applies partial reclassification by focusing computational resources on merchants whose data has changed or who are likely to have changed classification, rather than reclassifying all merchants uniformly. This selective approach maintains high classification reliability while minimizing the total processing time and computational resources required.
Data Source
AI summary
Techniques and arrangements for industry vertical classification of merchants using merchant signals, based, in part, on comparing the merchant signals with collected business class profiles. The merchant signals can include reported data, collected data, and third-party data associated with the merchant. The techniques can include identifying one or more business class profiles using the data associated with the merchant and classifying and/or reclassifying the merchant within a business class using the one or more identified business class profiles.