Maximising AP Efficiency with Machine Learning Invoice Recognition

Your accounts payable department is a critical business function. Without them, it’d be increasingly difficult to ensure your company meets its financial obligations on time while preventing cash flow issues.

In many cases, your AP staff is stretched thin, dealing with thousands of invoices from multiple vendors. And to make matters worse, they tend to spend most of their time on menial tasks, such as manual data entry, invoice data capture, and verification.

But, thanks to technological advances, you can now streamline AP operations and maximise the department's efficiency.

In this article, we’ll share why maximising AP efficiency should be your priority and provide tips on how to improve processing invoices with machine learning and AI invoice processing tools.

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Why Maximise AP Efficiency?

Simply put, maximising your accounts payable efficiency saves you time and money — two finite resources that are crucial in business.

But there are also somewhat hidden benefits to increased efficiency, such as better financial decision-making.

Let’s dive a bit deeper into these benefits: 

• Cost Savings: An efficient accounts payable process can help your company save money by reducing errors, preventing overpayments and duplicate payments, and avoiding late payment fees and interest charges.
  
• Improved Cash Flow Management: With an efficient accounts payable process, you can ensure that your company's bills are paid on time, which can help you manage your cash flow more effectively.
  
• Stronger Supplier Relationships: An additional benefit to improved cash flow management is better relationships with your suppliers. By paying them on time, you ensure their business can better manage their finances while putting you in a position to negotiate better terms in the future.
  
• Increased Productivity: A streamlined accounts payable process can help reduce the amount of time and effort required to process invoices and make payments. This frees your staff to focus on more value-added tasks, such as in-depth financial analysis or vendor relations.
  
• Better Visibility and Control: Besides improved cash flow, efficient AP processes also give you greater visibility into your company's financial operations. Having faster AP information capture allows for faster analysis, meaning stakeholders can make quicker decisions based on trends in the company’s financials.

Now, this is all well and good, but how can you actually go about increasing AP efficiency?

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How to Increase Accounts Payable Efficiency

While machine learning and invoice recognition are the focuses of this article, there are also other ways to improve AP efficiency at the same time:

• Establish Clear Policies and Procedures: Having clear policies and procedures for accounts payable can help ensure consistency and reduce errors in the payment process.
  
• Improve Communication with Suppliers: Establishing good communication with suppliers can help ensure that invoices are accurate and that payments are made on time.
  
• Monitor Accounts Payable Metrics: Tracking accounts payable metrics, such as invoice processing time and payment cycle time, can help identify areas for improvement.
  
• Regularly Reconcile Accounts: Regular reconciliation ensures that all payments and invoices are accurate and up-to-date, reducing errors and improving efficiency.

Once you have these in place, you can focus on implementing automated invoice processing to improve AP efficiency significantly.

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OCR Technology for Automating Invoice Processing

Automated invoice processing will help with large invoice volumes and reduce the need (and cost) of keeping physical copies of extracted data in storage.

Most AP departments start with Optical Character Recognition (OCR) software, and, while this does improve invoice processing efficiency, there are some issues.

One of the biggest is that OCR software is highly rigid regarding character recognition. For example, you must create an invoice template for every format you receive.

So, you’ll have to create templates for each invoice scanning each supplier and templates for each invoice medium, i.e., paper, email, fax, etc., which all but negates the benefits of automation.

And the more templates you add to your OCR software, the less accurate it becomes as it has to determine which invoice to tie to which template.

If that’s not enough, basic OCR document processors struggle with uncommon character types, requiring constant maintenance and template updates. Plus, they’re easily confused by typos.

That’s why the real way to maximise AP efficiency is by pairing OCR software with machine learning algorithms.

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The Role of Machine Learning in Invoice Recognition

Machine learning essentially gives OCR invoice processors a brain. It allows this software to actually understand what it’s processing, rather than relying on templates for instruction.

This makes it much more flexible when it comes to data extraction.

For example, OCR software struggles to extract data from forms and tables accurately. But adding machine learning into the mix allows it to parse many different invoice formats without breaking down.

Deep learning (the subset of ML that learns from its environment) differs significantly from traditional OCR. It utilizes both visual features and natural language models to achieve a more comprehensive understanding of entities. It can understand and differentiate the invoice line item from invoice numbers, or tax amount, even when dealing with different formats or large volumes of invoices.

Consequently, deep learning models are less susceptible to errors such as typos or misidentification of characters.

Besides that, the more you train your machine learning models and your OCR processor, the smarter it gets, and it can save time more efficiently.

Rather than invoice extractions becoming less accurate with the more templates you expose them to, the ML algorithm’s neural network continues to learn and adjust its parameters until it achieves an acceptable level of accuracy in its invoice extractions.

All this can be done with as much human intervention in invoice processing as the company wants. Even though ML and its neural networks excel at text detection and text recognition, some may want a bit of manual review added to the process.

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Improved Invoice Recognition is Just One Benefit of Pairing ML With OCR Software

Yes, using machine learning for invoice processing provides significantly lower error rates along with requiring less maintenance and human input.

But it can do so much more, including:

• Automatic Invoice Categorization: Machine learning algorithms enable automated categorization of invoices based on data such as vendor name, invoice date, and invoice amount, streamlining invoice processing and payment tracking.
  
• Duplicate Invoice Detection: These same ML algorithms can detect duplicate invoices by comparing new invoices against your existing database, preventing overpayments and reducing errors.
  
• Automated Payment Matching: ML also offers automated invoice reconciliation of your accounts receivable. By automatically comparing invoice data against completed payments, you can improve your AP efficiency while reducing human errors.
  
• Fraudulent Invoice Detection: Machine learning can detect fraudulent invoices by flagging discrepancies such as incorrect bank details or invoices that do not match previous invoices from the same supplier, helping to mitigate fraud risk.

Furthermore, if your software allows, you can even integrate additional ML algorithms to allow workflow automation, such as automatic invoice approvals and payments. It can also help you optimise payment windows to improve cash flow.

All this adds up to a lower error rate while improving AP processing efficiency, ultimately driving better financial decision-making.

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Is Maximising Your AP Efficiency a Priority for 2023?

If it hasn't been so far, the invoice automation process to increase your accounts payable department efficiency should definitely be one of your top priorities in 2023.

We witness the rapid expansion of different artificial intelligence tools and systems. However, this is not a new thing in the invoice processing world. Manual data entry and extracting data from PDF invoices and invoice images have been recognised as considerable productivity blockers for AP teams.

Some AP teams may feel that they are using the latest advancements in invoice processing if their accounting system is using:

• invoice templates and template matching
• optical character recognition (OCR)
• natural language processing
• automated data extraction

This is now considered a traditional method, one step above manual entry for invoice processing. However, to capture data and extract structured data efficiently, you need an AI invoice processing tool that uses:

• deep learning
• advanced OCR models powered by AI
• computer vision
• natural language processing

That is the best way to ensure that your AP teams get all the help they need to tackle time-consuming extracting data and invoice processing; you need to go for an AI invoice processing solution. That is rapidly becoming a benchmark for invoice processing.

Take the first step by trying Vega, our industry-leading AI engine that powers our data extraction software and API. Vega allows you to extract over 50 invoice fields in over 50 languages accurately and then export data from them straight to your accounting system for further analysis.

Try our ML-powered invoice extractor with a free trial today and discover why significant brands like McKinsey, Intuit, and Fidelity trust us with key data from their document processing.