Invoice Capture: In-depth guide to most firms’ first AI purchase

Invoice capture is a growing area of AI where most companies are making their first purchase of an AI product because it is an easy to integrate solution with significant benefits.

While digitization helped automate numerous processes, mostly rule based software was used in digitization. However, invoice capture involves both reading the invoice text with Optical Character Recognition (OCR) and understanding its context with machine learning.

What is invoice capture?

Invoice capture (also called invoice capture, invoice data extraction or invoice OCR) is extracting structured from invoices so invoices can be automatically processed.  Invoice capture has been the first back office process to be automated with AI for most companies. Read more

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Invoice Automation: First Process to Automate in 2019

Invoice automation (also called automated invoice processing) is a maturing area of automation with limited implementation risks and significant benefits. Invoice automation would free up back office finance/procurement teams to focus on higher value added tasks.

What is invoice automation?

Invoice automation allows straight through processing (no human interaction) most of the time for the entire invoice process. Invoice automation involves

  • monitoring for invoices: Invoices arrive in companies as PDFs, image files and increasingly rarely as hard copy documents.
    • For digital invoices, an RPA bot or a simple email automation tool can flag emails with invoices and forward them for data extraction. Some companies use a dedicated email address for invoices to further simplify invoice monitoring.
    • For hard-copy invoices, companies are switching to using a single address to centralize invoice scanning
    invoice capture: Extracting relevant details (e.g. bank account, ordered item) from the invoice. If software does not have confidence in the results, it is sent to employees for a manual check. evaluating invoice against order records and other criteria to ensure that the payment is indeed a valid one. Evaluations include
    • cross-checking invoice against purchase orders
    • cross-checking invoice for duplicity
    • using working capital optimization policies to decide payment time
    • using limits to to decide whether to manually process invoice. Invoices that are abnormally large compared to a suppliers’ usual invoices may need to be manually verified to ensure that wrong payments are not done
    recording invoice-related information in systems making the necessary payment to settle the invoice

    All steps except invoice capture are rule-based processes. However, invoice capture relies on machine learning to extract the data in the invoice. For more, please read our article on invoice capture. Read more

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