ML Model for Supplier Transaction Acceptance Prediction
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Solution Overview
Problem
Buyers face challenges in determining which suppliers accept certain transaction methods, such as credit card payments, due to the lack of publicly disclosed information and the time-consuming process of communicating with each supplier individually.
Innovation Solution
A machine learning predictive model is developed to predict the likelihood of a supplier accepting card transactions by training on inputs such as supplier data, card source information, previous model outputs, and outreach results, enabling automated and real-time transaction acceptability predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If buyers communicate directly with each supplier to ask about accepted transaction methods, then they can obtain accurate information about transaction acceptability, but the process becomes extremely time-consuming when dealing with hundreds or thousands of suppliers
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between buyers and suppliers. This system collects, stores, and manages transaction method preference data from suppliers in a centralized database, allowing buyers to query this information without directly contacting each supplier. The intermediary database serves as the mediation layer that resolves the time-cost issue while maintaining information accuracy.
Solution Approach 2:
The system performs preliminary action by having suppliers pre-register their transaction method preferences in the database before any buyer needs to inquire. This advance preparation eliminates the need for real-time communication when buyers need the information. The data is collected and organized in advance, so when a buyer queries the system, the answer is immediately available without requiring the buyer to contact the supplier at that moment.
2Loss of information
If buyers manually contact every supplier to determine transaction method acceptance, then they can ensure complete information coverage, but the complexity and resource requirements of the process increase significantly
Solution Approach 1:
The system implements universality by creating a single database infrastructure that serves multiple functions: storing supplier preferences, enabling buyer queries, supporting multiple transaction methods (card, check, wire, ACH), and accommodating numerous suppliers simultaneously. This universal platform replaces the need for individualized communication processes with each supplier, reducing overall system complexity while maintaining comprehensive information coverage.
Solution Approach 2:
The system uses copying by creating a digital representation of supplier transaction preferences in the database. Instead of requiring buyers to physically or verbally contact each supplier, the system copies the relevant information into a queryable format. This digital copy allows unlimited access and retrieval without additional complexity in the communication process.
3Ease of manufacture
If traditional methods are used to determine supplier transaction preferences, then the process can be simple in approach, but the productivity and scalability of the solution deteriorate when handling large numbers of suppliers
Solution Approach 1:
The patent applies mechanics substitution by replacing the mechanical process of human-to-human communication with an automated electronic system. Instead of buyers manually calling or emailing suppliers, the system uses automated database queries and electronic data retrieval. This substitution dramatically increases productivity while maintaining simplicity in the user interface - buyers simply input supplier identifiers and receive instant results without the overhead of manual communication processes.
Data Source
AI summary
Systems and methods for determining the likelihood of a supplier accepting a certain transaction method such as a payment card. Exemplary systems can generate a machine learning predictive model to determine the likelihood of a supplier accepting a card payment.