Nearly every small and medium-sized business has an accounting department that understands all too well how tedious and time-consuming manual invoice processing is. Some estimates put the cost of processing an invoice manually at $30, which comes with the added risk of human error. Not to mention the need for a complex filling system and extra storage.
Companies seeking to realize a more automated and digitized future know it's time to upgrade their accounts receivable (AR) and accounts payable (AP) departments. The reasons for this are glaring. On average, automated invoice processing:
Curious how automated invoice processing works, especially if you work with email scans and physical invoices? This article covers the benefits of automating invoice processing with AI, including the technology involved, common hurdles to success, and how to get started.
Optical character recognition (ORC) is the process of converting hard copies of typed, handwritten, or printed documents or images into editable digital files. OCR technology is used in a variety of settings, from digitizing physical documents in libraries to converting PDFs into editable Word files. Invoice OCR is a special type of OCR that is designed to recognize and extract information from invoices. In theory, invoice OCR technology can be used to automate the process of data entry, making it faster and more accurate than manual efforts. However, the technology frequently encounters limitations when used to process large volumes of variable invoices at scale.
Here are some of the most common limitations of OCR, and why the technology often fails to perform when applied to invoice processing specifically:
A high dependence on input quality is a common limitation of OCR has been documented in research that compared four popular OCR solutions. When measuring performance of Google Docs OCR, Tesseract, ABBY Fine Reader, and Transym, researchers found the platforms saw reasonable character extraction accuracy (79-88%) under normal conditions. However, when tested using blurred or skewed images, accuracy declined significantly to somewhere between 28-62%. This demonstrates that “off the shelf” OCR solutions from some of the biggest names in software come with inherent limitations that struggle to overcome the real-world variability found in invoices and other document types.
A Forrester survey found that each error in a paper invoice costs companies $53.50 to rectify. This can be compounded by OCR inaccuracies, which are especially impactful in accounting. Even seemingly high prevision, say 90% accuracy, implies that one out of every ten characters will be extracted incorrectly. For organizations extracting hundreds or thousands of invoices a month, this adds up to a large amount of inaccuracy. Companies will then need to reprocess their invoices manually to identify and remedy mistakes, largely defeating the purpose of automating invoice processing in the first place.
Many of the OCR tools available on the market today are point solutions designed for a single task: data capture. However, most companies would benefit from selecting a more comprehensive solution that can cover the entire accounts payable process. By selecting an AI platform built for unstructured data processing, businesses can avoid the pitfalls of investing in solutions that only solve a singular problem.
For example, super.AI’s platform combines OCR technology with human review to guarantee user defined accuracy levels of 99% or greater. Additionally, the platform is equipped to automate unstructured data processing for any type of data an accounts payable department might come across, streamlining not just invoice capture, but invoice approval, payment authorization, payment optimization, analytics, and more.
Conversely, some OCR come bundled as part of larger suites that include additional software users may not need but are forced to purchase. Although this could be beneficial in theory, assuming many of the other products that come with the bundle are useful, it is far less flexible and future proof than a platform built to process any type of unstructured information. Rather than get locked into a single solution (or many), leverage a platform that can grow and evolve alongside your business.
By pairing advanced OCR with a unified AI platform built for unstructured data processing, we can overcome the limitations described above. Super.AI’s platform gives users the ability to mix and match AI/ML models, as well as leverage third-party or in-house experts for human-in-the-loop (HITL) supervision, making it possible to accurately identify variable invoices, extract relevant information from them, and automate previously manual tasks. The platform’s advantages include:
Most invoices are processed using a combination of automation and manual effort. However, artificial intelligence can benefit invoice processing from soup to nuts by automating more of the process. This begins with digitizing paper documents (as well as processing digital ones), extracting relevant information from them, and then inputting that information into a centralized system for further action. Here are the key functions of AI-automated invoice processing in greater detail:
A market report found that 70% of all invoices processed globally are paper-based. These paper invoices must first be digitized so they can be analyzed by the system, while invoices that arrive in a digital format are sent for analysis immediately. To begin, regardless of invoice format (paper, image, or PDF), optical character recognition will be used to make sense of the invoice data. This task relies heavily on sophisticated pattern recognition that is facilitated by AI/ML models.
Invoices do not follow standards in terms of formatting or structure, so effective OCR software must be capable of correctly identifying important details such as tax ID, invoice number, gross amounts, and more on virtually any invoice.
After ensuring invoices are machine-readable, AI algorithms are used to extract and validate relevant data fields. This stage might involve further review from an embedded cross-checking tool or HITL supervisors. However, this depends on how well a specific OCR is performing in a given scenario. Just keep in mind that any bookkeeping error at this point in the process can have a snowball effect. It’s best to ensure that a system is satisfying accuracy requirements before scaling invoice processing and attempting to use the data to automate additional tasks. After data is extracted and validated, it is entered into a centralized system for storage and further processing.
Currently, most entities leverage semi-automated solutions to handle electronic invoicing based on Excel macros or XML schemas. However, invoice generation can also be automated by using artificial intelligence to automatically fill out invoice details such as company information, tax codes, cost center, and amounts. This process is simplified if a vendor and their customers use integrated systems; however, professional human accountants still validate invoices before registering or sending them.
Here are some additional benefits that come with automating invoice processing with AI:
According to a recent survey run by the Institute of Finance and Management (IOFM), dubbed the Future of Accounts Payable Study, 63% of practitioners anticipate a slight or significant increase in the use of their department's data throughout the enterprise over the next few years.
For accounting departments, automating invoice processing grants authorized users quick access to critical data. As a result, users can track consumption and cost metrics, gain visibility into spending and working capital, manage utility costs, benchmark performance, filter reports, reveal new opportunities, and much more. Although complexity and the heavily paper-based nature of invoicing makes it a difficult digitization and automation challenge, organizations that solve this problem with the right solution will continue to benefit for years to come.
Accountants are expensive and in short supply, leading to increased invoice processing overhead for businesses whether they are growing or not. Accurate, AI-automated invoice processing can help streamline the most routine aspects of accounting, saving experts headaches and companies money. As mentioned above, companies that automate invoice processing can expect up to 90% cost savings per invoice processed.
Fraud is among the greatest threats to traditional invoice processing, and it can be extremely damaging to any organization. A unified AI platform for unstructured data processing can be easily configured to detect forged, duplicated, Photoshopped, and manipulated invoices as they are being processed. This is a proactive approach to fraud protection that can save your organization time and money.
Humans are often resistant to change, especially when it is unclear how it will impact our lives or the world broadly. When professionals encounter terms like "automation" and "artificial intelligence," they immediately think they will soon be out of a job. However, this isn’t the case with automating invoice processing with AI.
Although advancements in machine learning will eliminate routine, repetitive accounting tasks, this isn't a real loss for human employees. Most of the time, this only means eradicating stressful operations that complicate invoice processing, making accounting work more tedious and less appealing.
Instead of replacing them, accounting professionals should think about AI as a tool that helps them do their work faster and with greater accuracy and efficiency than ever before. This not only simplifies or removes arduous aspects of the job, but frees up time and brain power to focus on higher-level accounting matters like budgeting.
It is clear that automating invoice processing with AI can benefit organizations large and small. This advanced approach to invoice processing reduces manual data entry, lessens human involvement in repetitive tasks, cuts the risk of error, and delivers unmatched precision. Many organizations have successfully leveraged the super.AI platform to solve unstructured data challenges like invoice processing. To learn more and get started with AI, check out the following resources:
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