Automating Invoice Processing with Machine Learning

How we trained a machine learning model to automate and improve document processing while significantly reducing manual efforts.

Key Takeaways

Automated invoice data extraction from vendor emails
Improved accuracy and reduced errors via ERP validation
Saved time by reducing manual invoice processing
Trained machine learning model for document processing needs

The Challenge

Our client is a prominent manufacturer and distributor of hardware products in the United States. They receive a high volume of invoices from several vendors, each requiring a payment request form. However, the manual system in use was inefficient.

Employees had to review each invoice individually, which came in various formats and systems, before verifying it against their ERP system and issuing a payment request. This process consumed a significant amount of time, and to reduce man-hours, the client began exploring an automated system.

The Solution

At Rapidops, we used our expertise in machine learning to create a Form Recognizer that could automatically identify the format of incoming forms and extract relevant information for cross-checking with the client's ERP. By automating this process, we reduced the time, effort, and cost required to process each payment. Our client was pleased with the results - they only had to review a handful of forms manually, as opposed to every single vendor form against POs. We take pride in delivering such game-changing solutions to our clients and are committed to continuing to innovate and push the boundaries of what's possible.

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Data Ingestion & Transformation

Our team successfully handled hundreds of acknowledgment forms sent by the client's vendors via emails by leveraging cutting-edge technology and innovative techniques. Using our AI-powered solution, we were able to efficiently extract, transform, and load the data into the Azure Data Lake, enabling the client's AI models to consume it seamlessly. Our approach not only saved time and resources but also ensured the accuracy and consistency of the data, resulting in better insights and decision-making for the client.

Model Development

Model Training

Application Delivery

Our machine-learning model, trained on Azure Databricks, easily extracted data from acknowledgment forms through the Azure Form Recognizer API. We added labels to the dataset, enabling the model to recognize acknowledgment form layouts and extract data to an Excel sheet without further annotation. The model generated a confidence score based on the accuracy of the extracted data, highlighting any discrepancies for quick resolution by our automated process.