OCR and Machine Learning: A Winning Duo for Better Invoice Management

Managing invoices from suppliers is a key task for many businesses. It usually involves checking and processing these invoices, but doing it by hand can be slow, costly, and sometimes lead to mistakes. Thankfully, new technology like machine learning – a smart computer method that learns from patterns – can make things much better.

Before we dive into how machine learning helps, it's important to understand a big hurdle in managing invoices: pulling out and using the data they contain. Getting data from invoices is crucial because it feeds into machine learning systems, helping them get smarter. There's a tool called Optical Character Recognition that helps with this. It's like a digital reader—it scans invoices, picks out important details like dates and amounts, and reduces the need to type in this information by hand, which also cuts down on mistakes and saves time.

So, what's next after you have all this data? Machine learning comes into play by examining the data for any irregularities. It's trained to spot if something doesn't look right on an invoice, comparing it with past data and the rules a business already has in place. If an invoice looks odd, the system will flag it for a closer look. This helps prevent errors from slipping through and lets your team focus on more important tasks.

Machine learning can also help make sure that invoices get approved faster. It looks at past approvals to guess which invoices will likely be okayed without a hitch. This helps businesses plan better, avoid delays, and keep work flowing smoothly.

There's also a smart way to use machine learning to save money. It can spot the best times to pay invoices so that a business can take advantage of early payment discounts, boosting its cash flow with little extra effort.

Over time, as machine learning systems handle more invoices, they get even better at their job. This means they can adapt and grow alongside a business, becoming more helpful and accurate as they go. This is a big plus, especially since people often worry about new technology changing their workflow.

Machine learning is just one part of the broader field of artificial intelligence, which has been around for quite some time. It’s not a new concept, but it's becoming more important as it improves. When paired with tools like Optical Character Recognition, businesses can get more out of their data, and make the most of these technological advances.

While the promise of a 'touchless' invoicing system is enticing, achieving a high rate of automation, such as 90% touchless processing, varies by industry and depends on how well suppliers can meet certain standards. These standards might include specific invoicing formats or detailed vendor requirements. Moreover, the complexity of a company’s purchase order (PO) process can also influence this rate.

Given these specific conditions, a more realistic initial target for many companies would be to hit a touchless transaction rate of between 65% to 75% within the first six months. However, with continued optimization and as vendors become more accustomed to the system's requirements, reaching the 90% mark is feasible within a timeframe of one to one and a half years.


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