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BreadcrumbResourcesGuide to invoicing for Australian businesses

The future of invoicing

What does the future hold? Here are some likely developments in invoicing.

Invoicing has changed dramatically over the past decade, from paper invoices sent by mail and paid by cheque, to auto-generated e-invoices that can be paid instantly online. What does the future hold? Here are some likely developments in invoicing.

Machine learning and automated invoicing

Technology will continue to shape how payments are demanded and collected in the years ahead. Paper invoices will become ever-more rare as more businesses move to digital processes. Bills will be generated immediately, with payment options integrated into the invoice itself. Billing techniques will become more refined, customisable and intuitive. This will benefit both businesses and customers.

But an even greater shift is taking place. Machine learning allows invoicing systems to analyse and digitise invoices received, extracting the relevant information to speed up and automate payment processing and accounting.

In addition, blockchains, the building blocks which power cryptocurrencies such as Bitcoin, are increasingly being used for invoice reconciliation. This allows for fast, transparent, secure processing of payments, but for now it's in the early stages.

Machine learning software

Machine learning software can scan keywords and numbers on incoming invoices, recognising company names, invoice numbers and money owed. These features are already available, if not widely used.

Relevant information is automatically extracted from the received invoice, removing the need for manual data entry. So invoices are matched to purchase orders, and the individual giving final approval is informed and can confirm payment in a single click.

One provider of this type of software, French firm AODocs, estimates that companies can save 15 minutes of employee time per invoice using this digitised approach, which is a significant amount for businesses of any size.

These analysis systems extract data from documents, so the need for human intervention is almost entirely removed. This improves the speed, efficiency and accuracy of invoice management. It also helps with business analytics because the captured information is automatically filed into databases.

Machine learning isn't perfect. Such systems often struggle with invoices that don’t have a set format. Accuracy can be high when processing text and numeric data, but less reliable when trying to make sense of graphics, tables or visual features such as logos. This is improving as the technology develops, but full automation of invoice processing is a little way off yet.

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