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Xero launches machine learning automation to improve coding accuracy for small businesses
Posted on 12-Oct-2017 15:45 | Tags Filed under: News



Building on the success of its first machine learning project, Xero announced the next step in its journey towards code-free accounting – the automation of account codes and bills for all small businesses and their partners.

 

With more than 500,000 bills entered into Xero every day, and each line of a bill edited individually, the automation of billing account codes is set to transform accounting practices, ensuring greater accuracy and reducing the time small businesses spend creating bills.

 

While bills is the second-most commonly used feature of Xero, it has the second-highest rate of defaults, with every small business using the system differently. Fifty percent of businesses use 10 or more expense codes, while others create their own codes, which means that information is often entered incorrectly. Xero’s new artificial intelligence system will consider each individual business’s characteristics, then recommend account codes based on what it has learned.

 

“We set out to further develop our machine learning program to reduce the number of mistakes being made when creating bills. In doing so, we’ve created a system that not only learns from the individual needs of our customers, but can also make objective decisions about which account their transactions should be coded to,” said Andy Neale, Head of Data Science and Automation at Xero.

 

With accuracy at the forefront of Xero’s latest development, the account code automation is rolling out to all Xero users, with customers continuing to code their accounts for bills as normal. A minimum of 150 bills is required to be entered and as more bills are entered and accepted or corrected, the better the suggestions become.

 

“Account coding has the potential to impact both small business finances and reporting. So, when applying machine learning to Xero’s billing system, we needed to ensure accuracy was at the forefront of any changes we were making. While we’ll only know the true impact of the technology when people start using it, on average, we know we can achieve more than 75 percent accuracy after 150 bills have been entered.”

 

“Account codes can be time intensive task for small businesses. We are using machine learning to simplify the system overall, so that more time is spent growing the business, rather than on low-value, administrative tasks,” Neale said.

 







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