Pre-trained machine learning (ML) solutions: Companies build automation solutions based on millions of invoices.
Ideally, automation solutions should not create new manual tasks for users.
Using templates creates a code base that needs to be maintained.There are many different structures for invoices and these structures tend to change over time.However, they are no longer relevant since These solutions were prevalent before the recent rise of machine learning solutions. Template based solutions: End-user inputs the document structure to the software.In the invoice automation landscape, there are 3 types of solutions: Source: Amazon AWS Textract What are different types of invoice capture solutions? Invoice capture solutions can recognize these itemized lists and process them. Most invoices include an itemized list of services or products provided. Source: Amazon AWS Textract Capturing tables Invoice capture solutions can extract key value pairs from documents. Invoices include key value pairs such as company name, bank account number etc. While OCR captures text, invoice capture solutions capture key-value pairs and tables which are required to auto process invoices. What are the differences between invoice capture and OCR? By capturing all data on an invoice, invoice capture software enables companies to run compliance checks on invoice data. improves compliance: Invoices hold numerous data fields which are traditionally not captured manually.In case the company discovers that data extraction had faults, these documents can be used to understand the source of errors which can be corrected for future invoices. allows auditability: Invoice data can be stored with bounding visual boxes that show where data was extracted from the invoice.allows faster turn-around time which prevents the unnecessary back and forth between suppliers and the business which consumes valuable employee time.allows employees to focus on higher value added activities.helps reduce back-office costs by reducing manual effort.What are the benefits of invoice capture?
Feel free to check this article where we also explained other hyperautomation examples. Invoice automation is also an example of hyperautomation and we have explained how hyperautomation works for p rocesses triggered by incoming documents or email before. How do machine learning powered invoice capture solutions works?Įnd-to-end automation of processes is possible thanks to hyperautomation which is the technology that combines different technologies such as AI, OCR and RPA. To ensure that wrong payments are not made, suspicious invoices and invoices that require payments beyond a certain limit would need to be reviewed by humans. If data extraction is deemed to be successful, data is fed to the record keeping and payment systems.Ĭompanies need to set up quality assurance processes in any automated process where errors can be costly. If there is significant uncertainty about the data, a human is notified to take a look at the invoice. For more on different types of invoices, feel free to read our article on invoices. Invoices that arrive via EDI can be auto-captured since they are already in the form of structured XML files. This is only relevant for invoices that are received outside of an Electronic Data Interchange (EDI). Invoice capture has been the first back office process to be automated with AI for most companies. Invoice capture (also called invoice data extraction or invoice OCR) is extracting structured data from invoices so invoices can be automatically processed. We answered all your invoice capture related questions: What is invoice capture? Invoice capture involves both reading the invoice text with Optical Character Recognition (OCR) and understanding its context with machine learning. While digitization helped automate numerous processes, mostly rule based software was used in digitization. This is because invoice capture is an easy to integrate solution with significant benefits. Invoice capture is a growing area of AI where most companies are making their first purchase of an AI product.