Optimising the Accounts payable (AP) process has been a difficult and frustrating undertaking for many companies. Despite efforts in process re-engineering and the use of technologies such as ERP, OCR, and EDI document management and e-invoices, fundamental issues remain.
- Non-standard invoicing
- Unstructured data
- Discrepancies & approvals
For an influx of unstructured data to become structured, an automated flow of digitised structured data must be established so that a company’s ERP system seamlessly integrates with automated activity outcomes and data.
These steps also provide a solid automation foundation for organisations with a highly manual procure-to-pay process (P2P) cycle, enabling them to reduce costs, improve performance, achieve regulatory compliance, and gain real-time visibility into payables status.
Automation can help the team focus on other activities within the P2P process, such as improvement of supplier relationships to assure on-time delivery.
- Scanning a mailbox for orders and entering them in ERP system
- Matching the purchase order with the invoice
- Generating purchase order after purchase requisition approval and more
Bytelogix combines robotic process automation (RPA) and artificial intelligence (AI) to help you extract and interpret data from different documents and ensure end-to-end document processing. The tool works with a wide range of documents , recognises different objects like tables, handwriting, signatures, or checkboxes, and can deal with various file formats.
In the case of any exceptions, robots ask for help to confirm the data. An employee receives a notification and opens Validation Station to validate data and handle exceptions in the extraction or classification results. Here you can also review all the extracted fields if needed to make sure the data is processed accurately.
Machine learning models help robots understand the specificities of your documents. Similarly, the models can be retrained automatically based on the human input in the Validation Station . This means that the more you work with the model, the more effective it becomes. Thus, the accuracy of the output improves over time.