Automated Invoice Approval via Machine Learning Prediction
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Solution Overview
Problem
In business-to-business (B2B) transactions, extended payment terms and late payments create friction due to laborious invoice approval processes and inefficient electronic systems that require manual entry of virtual card transactions, slowing down payment processing.
Innovation Solution
A system utilizing machine learning to predict invoice approval without buyer intervention, automatically generating virtual card numbers for instant payment processing through email interception or Straight Through Processing, eliminating the need for manual data entry and speeding up payment transfers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If virtual card payments are used in B2B transactions, then payment speed is improved, but manual data entry requirements create inefficiency and delays
Solution Approach 1:
The system performs automatic data extraction from invoice documents using OCR and machine learning algorithms, eliminating the need for manual data entry by suppliers. The system self-services by automatically capturing invoice details, validating them, and processing payments without human intervention in the data entry phase.
Solution Approach 2:
The patent replaces manual mechanical data entry processes with automated optical character recognition (OCR) and machine learning-based data extraction systems. This substitution transforms the manual typing process into an automated digital recognition and processing system, significantly reducing time and effort.
2Productivity
If invoice approval processes are streamlined for faster payment, then payment efficiency is improved, but approval accuracy may be compromised
Solution Approach 1:
The system implements multiple validation and verification steps including automated invoice matching against purchase orders, anomaly detection algorithms, and exception handling protocols. These feedback mechanisms ensure that while processing is automated and fast, accuracy is maintained through continuous verification and correction loops.
Solution Approach 2:
The system performs preliminary validation and verification of invoice data before final approval, using machine learning models to pre-assess invoice legitimacy and flag potential issues. This preliminary action ensures that only properly validated invoices proceed to payment, maintaining accuracy while enabling fast processing for approved invoices.
3Reliability
If traditional invoice approval systems are used, then payment security is maintained, but processing time is excessively long
Solution Approach 1:
The system dynamically adjusts the approval workflow based on invoice characteristics, supplier history, and risk assessment. Low-risk invoices from trusted suppliers with consistent billing patterns can be approved automatically with minimal review, while high-risk or anomalous invoices receive enhanced scrutiny. This dynamic approach maintains security while reducing average processing time.
Solution Approach 2:
The system performs preliminary risk assessment and validation before the formal approval process, pre-qualifying invoices that meet all security criteria. This preliminary action ensures that secure invoices are already vetted when they reach the approval stage, maintaining security protocols while eliminating redundant verification steps for trusted transactions.
Data Source
AI summary
The disclosure herein relates to methods and systems of that facilitate instant payment of invoices based on automatic, intelligent, predictions that a buyer will approve an invoice without express input by the buyer. For example, a system may determine a probability that an invoice will be approved for payment by a buyer with express approval from the buyer. Once an invoice is automatically approved for payment, the system may employ various automated systems to facilitate payment to suppliers without the need for suppliers to extract virtual card data or other payment information from email payment requests or other invoice payment requirements to receive payment. For example, the system may generate calls to automatically generate virtual card numbers on behalf of buyers and submit payment messages for payments to suppliers using the virtual card numbers.