Invoice Scoring Platform for Buyer-Free Supply Chain Finance
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Solution Overview
Problem
Traditional supply chain financing systems require manual invoice approval by the buyer, which is unpredictable, labor-intensive, and creates bottlenecks, limiting scalability and accessibility to smaller suppliers.
Innovation Solution
A finance management platform that automates invoice processing and scoring using machine learning and algorithmic models to determine financing eligibility, eliminating the need for buyer approval and enabling immediate payment to suppliers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual invoice approval by the buyer is implemented, then risk control is improved, but processing time and labor intensity increase
Solution Approach 1:
The system performs preliminary risk assessment and scoring of invoices before they reach the buyer for approval. By pre-evaluating invoice risk using automated scoring models, the system prepares risk information in advance, allowing buyers to make faster approval decisions without compromising risk control.
Solution Approach 2:
The patent replaces manual mechanical review processes with automated electronic scoring systems. Machine learning models and algorithms automatically evaluate invoice risk, substituting human manual assessment with computational analysis, thereby reducing processing time while maintaining or improving risk control accuracy.
2Reliability
If manual invoice approval processes are used, then risk assessment is improved, but scalability and accessibility to smaller suppliers worsen
Solution Approach 1:
The system enables self-service risk assessment where invoices are automatically scored and evaluated without requiring manual buyer intervention for each invoice. This automated self-assessment capability allows the system to scale efficiently while maintaining consistent risk assessment standards across all suppliers regardless of size.
Solution Approach 2:
The patent changes the parameters of risk assessment from manual qualitative evaluation to automated quantitative scoring. By transforming risk assessment into measurable parameters that can be processed algorithmically, the system achieves both reliable risk assessment and scalability to handle large numbers of suppliers including smaller ones.
3Reliability
If buyer approval is required for financing, then financing risk is controlled, but processing efficiency and speed decrease
Solution Approach 1:
The system performs preliminary financing eligibility assessment and risk scoring before the buyer approval stage. By pre-determining which invoices meet financing criteria through automated scoring, the system reduces the workload for buyer approval while maintaining risk control, thereby improving overall processing efficiency.
Solution Approach 2:
The patent extracts the risk assessment and financing eligibility determination functions from the buyer approval process itself. By separating these functions into independent automated scoring systems, the buyer approval process focuses only on final authorization, improving both risk control and processing speed.
4Reliability
If traditional SCF systems are implemented, then large corporations benefit from lower financing rates, but implementation and management costs outweigh advantages for smaller entities
Solution Approach 1:
The system creates a universal financing platform that serves both large corporations and smaller suppliers through the same automated processes. The multi-functional scoring system handles diverse invoice types and supplier sizes uniformly, eliminating the need for separate complex implementation processes for different entity sizes.
Solution Approach 2:
The patent replaces complex manual implementation and management processes with automated electronic systems. Machine learning models and algorithmic processing substitute for manual system setup and management, reducing implementation complexity and ongoing management costs while maintaining financing rate advantages.
Data Source
AI summary
A finance management platform and method are disclosed. A model element data repository encodes a plurality of model elements representing invoicing outcomes, each model element including a score value. A processor is configured to execute computer program code for providing a finance management platform for a plurality of clients, including receiving invoicing data from a client data repository remote from the finance management platform, translating the received invoicing data into a common format and store the invoicing data in the common format in an invoice data repository, accessing the model element data repository and determine one or more of the model elements applicable to the invoicing data, calculating an overall score for the invoicing data from the applicable model element's scores and triggering a communication to a payment processor and an update to the invoice data repository upon the overall score exceeding a predetermined threshold.