Invoice processing plays a significant role in any business, but it can be a time-consuming and error-prone task, especially when done manually. To prevent time loss and potential mistakes that arise along the way, you can implement artificial intelligence (AI) and machine learning (ML). 

Gartner predicted that by 2025 at least 50% of invoices exchanged by B2B companies will be paid without the need for manual intervention. Considering that there have been a few years of speedy developments in the AI and ML fields since this prediction, the impact may be even larger. Through this article, we’ll discuss the actual benefits and use cases for AI and ML in invoice processing.

5 Benefits of ML and AI in Invoice Processing 

Financial processes require maximum attention, but AI can handle such tasks in a fraction of the time we need. Generative AI in finance can automate risk assessment, optimize investment strategies, and improve customer service through personalized financial recommendations. The surprising find is that using AI and ML for invoice processing not only helps the finance department but increases productivity in other departments too. This is just one of the five prevalent benefits of using AI strategy and ML in invoice processing. 

1. Improved Accounts Payable Workflow  

Paying vendors and creditors on time is an essential duty of the accounts payable department, and lagging behind with payments obviously delays business and affects your relationships with creditors. Now, with the integration of AI and ML, building improved automation workflows for accounts payable processes is made much easier than before. 

The reason for an obvious increase in efficiency is quite clear: artificial intelligence possesses cognitive capabilities to capture data in a blink, eliminating the need for manual data entry. All you need to do is organize a professional data generation process so that your future AI model can learn it properly. Since the time needed for processing invoices reduces, tasks are completed much faster. Also, AI brings constant workflow improvements because it progresses with each invoice it processes. 

2. More Accurate Automation 

Tools powered by machine learning and artificial intelligence offer unmatched accuracy compared to the human eye. The chances of duplicate payments, misinterpretations of invoice data, and data entry errors are low. Furthermore, such tools are capable of detecting suspicious and fraudulent invoices and marking them for further analysis and approval from a real person. 

Financial data is composed of numbers, which are an algorithm’s love language. Therefore, achieving accuracy in number-related tasks is a no-brainer. By providing templates and instructions, you can train these intelligent machine-learning algorithms to extract data and perform invoice processing actions with accuracy. Over time, these tools get constantly more accurate because they learn from every invoice they process. 

3. Higher-Quality Data Collection 

Higher accuracy ultimately leads to higher-quality data by combining Natural Language Processing (NLP) and Optical Character Recognition (OCR) with smart and self-learning algorithms. Once the process is automated, invoicing happens with minimal human intervention and without compromising quality.

Besides instructing AI and ML to extract invoice data according to specific conditions, you can combine it with different APIs and perform other tasks, such as currency conversions, discount calculations, etc. By reducing the number of tasks you have to complete manually, you have more time to double-check the produced results and ensure quality.

4. Increased Productivity across Departments 

Task quality is often the reason behind productivity or the lack of it. AI can guarantee higher-quality tasks for your teams by automating all the boring, stressful tasks that involve typing, invoice routing, and data entry. Your team can now focus on analyzing performance and deriving insights, budget planning, and nurturing customer relationships. 

As a result, you’ll have a more productive team, and this means they will have more time to cooperate with other departments and offer the needed insights on time. Other departments will experience an increase in productivity as a consequence of this chain effect. (At the rate at which the AI industry is helping us automate processes, finding someone’s email address might be the only challenging task left for us. But when you think about it, even that is automated. :D)

5. Better Overall Cash Flow 

A business’s overall cash flow can improve with the use of AI and ML since they accelerate the usual services and shorten the time it takes to produce results. AI also makes it easier to respect deadlines and maximize the potential to earn early payment discounts. 

By automating your invoice processing steps, there will be fewer penalties for late payments or other additional fees affecting the overall cash flow. A streamlined payment processing system powered by the predictable nature of AI and ML makes it possible to finalize reports, pay invoices, and avoid fees easily.

How Machine Learning and AI-Based Invoice Processing Helps Your Business 

We’ve mentioned some of the benefits of using AI and ML for the overall financial health of your business, but what are some actual use cases that serve as examples? Well, here are a few situations in which ML and AI can aid you:  

Make Invoice Categorization Easier 

One of the crucial tasks in invoice processing is categorization. All the received invoices need to be read and then rerouted to the respective person or department. Using machine learning algorithms, the process of categorizing invoices based on certain data (such as the amount, date, or vendor name) gets done extremely fast. 

The algorithms can rely on predefined rules or analyze historical data to categorize invoices, among other approaches. Such categorization makes it obvious which invoices need to be paid soon or how many invoices are to be paid overall. When dealing with a large number of invoices, such solutions can be life-saving for the business. Speeding up invoice categorization accelerates all the other business processes. 

Facilitate Duplicate Invoice Detection  

Duplicate invoices appear innocent, but they can do harm to a business. If they’re not detected, you end up paying the same invoice twice. While clients send invoices more than once through several channels to be sure, the issue of duplicates arises. Expenses can increase to unfathomable amounts if these duplicate invoices aren’t identified and sorted out. 

For instance, experts reveal that Medicare was reported to have paid over $89 million in duplicate invoices back in 1998. That’s a huge amount! But such duplicates can be avoided by employing machine learning algorithms that can instantly detect duplicates by comparing invoice details, like the amount, vendor name, and date. 

Match Invoices to Payments Faster  

Another useful case of ML and AI in invoice processing is to match payments with invoices. When the process is done manually, it takes an ample amount of time, but done with AI and ML systems, it’s fast and accurate. 

Businesses can reconcile their accounts receivables pretty fast by incorporating AI into their processes. ML and AI use details (such as invoice amounts, vendor names, and payment dates) to quickly and efficiently match invoices to payments. Not only does this accelerate financial operations but also makes it easy to resolve any possible discrepancies. 

Predict Invoice Payments Seamlessly  

Machine learning and AI work on different aspects of invoice processing, and another process is the ranking of invoices based on the timeframe in which they should be paid. Predicting invoice payments with machine learning removes the guesswork and leads to more informed decisions.

Respective algorithms are trained on the historical data of previously paid invoices by taking into consideration variables such as vendor names, invoice amounts, and dates. Eventually, regression models are trained on the data and offer nearly accurate predictability that guides businesses toward forecasting their cash flows more wisely. 

Aid in Approving Invoices  

Classification models are fed on data used to train them on which invoices to reject and which ones to approve based on the vendor, date, and amount. Such a process can run on autopilot once the algorithms are trained. 

Businesses won’t need to waste man hours on manual reviews and approvals of invoices and can focus instead on other tasks. Still, there might be cases of complicated invoices that can’t be predicted with full accuracy and might require double-checking from a real person. 

Help Extract Invoice Data 

Extracting invoice data is one of the most tedious tasks, but by using machine learning algorithms, you can automate the process with ease. Not only does this extraction of invoice data speed up the process, but it also reduces potential errors. There are several technologies, like OCR and AWS Textract, that you can use to perform invoice data extractions. 

Other solutions, like CNN-based models, can also be used for higher accuracy. CNN models are better for extracting information from invoice images, while AWS Textract functions best on scanned paper documents, images, and PDFs. AWS Textract is a more advanced version of the simple OCR software provided by Amazon Web Services, but it’s not the only solution.

Whether you’re extracting data from paper invoices or using a form builder to receive invoice data, you can always incorporate AI and ML for more efficient workflows. Focus on automating invoice processing, but always have someone double-check its work to steer away from risks.

Conclusion 

In the revolutionary era of AI, we must catch up with the digital transformation wave and benefit from the automation offered by ML and AI. Even though invoice processing contains great sophistication, and mistakes can lead to drastic losses, you can benefit if you complement it with human provision. Implementing ML- and AI-powered solutions into invoice processing will reward you with extra time and resources you can invest elsewhere.