Invoice processing powered by artificial intelligence (AI) can help accounts payable departments speed up and streamline the end-to-end management of invoices. This includes automating key tasks, such as receiving invoices, recording invoice details, approving invoices and triggering automated workflows for routing invoices through payment processing and financial reporting.

This guide provides insights on how companies can use AI models to automate invoice processing and eliminate manual work, as well as the technologies involved and the advantages of investing in AI-powered invoice processing solutions.

What Is AI Invoice Processing?

AI invoice processing uses machine learning (ML) algorithms powered by optical character recognition (OCR) to capture text from invoices and extract relevant information. After data extraction, AI/ML models seamlessly automate the process of verifying and validating invoice data, mapping it into common data fields and entering it into financial systems. Additionally, AI invoice processing can help businesses establish intelligently automated workflows for either auto-approving invoices in accordance with accounts payable policies or sending automatic reminders to approvers when human checks are necessary. AI invoice processing also streamlines and automates the handling of exceptions by detecting errors and exceptions and sending them to the proper channels for oversight and correction.

When done well, automated AI-powered invoice processing can speed up invoice processing time, reduce the cost of labor and increase the ability of accounts payable staff to handle mounting volumes of invoices as a business grows. Yet 48% of invoices are still received through manual, paper-based processes, and electronic invoices are received in disparate formats.

Key Takeaways

  • Businesses struggle to accelerate the processing of unstructured and differently formatted invoices through traditional automation systems.
  • AI technologies, such as ML and natural language processing, use advanced pattern matching to extract data from invoices and map information to common data fields used by financial systems.
  • AI invoice processing significantly reduces processing time and cuts the cost of invoice handling.

AI Invoice Processing Explained

Before the advent of AI, one of the biggest stumbling blocks of automated invoice processing was inconsistent invoice formatting. Most businesses today must contend with a wide variety of invoice formats that use different numbering conventions, put vendor address and contact details in different places, reference purchase order (PO) numbers in varying ways and break down line-item costs using disparate formats. What’s more, these invoices can be submitted as paper hard copies, PDFs, word processing documents, spreadsheets or in emails. While some larger enterprises may have the clout to impose upon their vendors a standardized invoicing format or mandated use of a structured e-invoicing platform, this isn’t typically feasible for small and medium-sized businesses. And even in today’s digital age, only about half of all invoices are received electronically.

All of that information floating around in different invoice formats can impede automation capabilities, which work best when they’re used to process uniformly structured information. Without AI, it can be challenging to automate both the accurate extraction of all relevant unstructured data from invoices and the consistent input of this information into accounting systems or other enterprise platforms for further automated payment processing and analysis. This is why so many accounting departments have historically needed human intervention to accurately handle invoice processing. And this is where AI comes in: The power of ML algorithms and natural language processing (NLP) makes it easier to automatically and accurately detect and record relevant data, such as invoice numbers, dates, total amounts and vendor details, from each invoice, no matter the format or form factor in which it’s delivered.

AI can play a valuable role in automating other stages of invoice processing as well, including data verification, by automating the cross-checking of ingested invoice data against existing records, such as POs and receipt-of-goods notes. Similarly, AI enhances automate approval workflows by automatically sending invoices to the appropriate approvers and providing guidance about what actions they need to take. Once routed through approval, the AI invoice processing system queues up payment per the invoice terms and business policies for each individual vendor relationship.

AI invoice processing makes it possible for automated-exceptions workflows to trigger manual reviews when potential problems are identified with certain invoices. This includes AI-powered detection of billing errors that could lead to over- or underpayment, as well as the identification of invoicing patterns and behaviors that could indicate potentially fraudulent activity. When an exception is found to have been caused by the way AI processed the invoice, the system will give the manual reviewer the opportunity to train it to learn how to correctly handle similarly formatted invoices in the future. This self-learning component is a hallmark of AI. In time, AI can help accounts payable teams drastically reduce the time and cost involved in invoice processing. The most recent industry estimates show that the best in the business are able to process invoices 81% faster and achieve 79% lower invoice processing costs.

Technologies in AI Invoice Processing

AI invoice processing involves a range of technologies that make it possible to not only extract data from individual invoices and route it through the payment process, but also to apply predictive analytics to this data once it has landed in financial systems. The following are three of the most common technologies used in AI-powered invoice processing.

  • Optical Character Recognition (OCR)

    Optical character recognition (OCR) converts scanned or digital invoice images into machine-readable text. The OCR engine scans the invoice images; identifies text characters, numbers and symbols; and extracts relevant information, such as vendor name, invoice number, line items, amounts and dates. This extracted data is then processed by AI/ML models to further enhance accuracy, validate entries and map the information to appropriate fields in accounting software or enterprise resource planning systems, enabling automated invoice processing with minimal human intervention.

  • Machine Learning (ML)

    ML enables AI-powered invoice processing systems to learn from historical invoice data captured by OCR and continuously improve the accuracy of identifying relevant information. ML algorithms can identify patterns, adapt to diverse invoice formats and layouts, and make accurate predictions for data extraction, validation and classification tasks, such as automated bill coding and general ledger code assignment. This self-learning capability allows the system to reduce manual intervention over time and provide a scalable solution for handling increasing invoice volumes across different suppliers and formats.

  • Natural Language Processing (NLP)

    NLP is typically paired with OCR to help accurately identify what captured text actually means. NLP is based on AI/ML algorithms that are trained on human language and communication patterns to interpret meaning and relationships of text within documents. For example, NLP is what helps an AI invoice processing system determine whether a number captured by OCR is a date or a dollar amount or whether words represent a vendor’s name and address or some other important detail. NLP allows an AI-powered invoice processing system to handle diverse invoice formats, languages and writing styles accurately, reducing the need for manual intervention and improving the overall efficiency of the invoice processing pipeline.

How AI Invoice Processing Works

AI invoice processing combines advanced pattern matching and NLP to turn invoices into machine-readable data and combines that with the power of automated workflows to help businesses cut down on manually intensive data entry and accounting verification work. It does this over the course of the following six steps.

  1. Invoice Capture

    The first step of AI invoice processing is invoice capture, an integral task for paper-based invoices and invoice images that come by way of PDF documents. In this step, businesses typically use OCR technology to scan and convert invoice images into machine-readable text. Some businesses that deal with hand-written invoices may also lean on intelligent character recognition in this phase, a more advanced capture technology used for converting handwritten characters to text. Invoice capture is a crucial step for transforming unstructured invoice images into structured data that can be entered into appropriate fields within accounting databases and enterprise platforms.

  2. Data Extraction and Interpretation

    Once invoice capture is complete and invoice information has been converted to machine-readable text, businesses can use a combination of NLP and ML algorithms to extract relevant data for accounting and billing purposes. Simple ML algorithms can do template-based extraction to recognize common invoice templates and pick up on common fields, including invoice number, line items, total amount and taxes. For invoices that don’t follow a common template, NLP and ML-based pattern recognition algorithms will sift through the text and extract those fields based on contextual language clues and text patterns.

  3. Validation and Verification

    AI-based tooling streamlines the process of validating and verifying the data extracted from invoices, not only for accuracy but also for compliance with contractual terms and accounts payable policies. AI tooling consistently cross-checks extracted data against predefined rules and existing databases to ensure that vendor details match up with a master list in order to detect duplicate invoices, conduct date validation and verify that the sum of individual line items matches the total amount billed. For many businesses, getting AI technology to automate the process of two- or three-way matching is necessary to reduce manual work and ensure consistent checking against POs and receipts. Finally, ML pattern matching monitors the stream of invoices processed by an accounts payable department to quickly detect anomalies that could indicate fraudulent behavior or other invoice discrepancies.

  4. Approvals and Exceptions Handling

    AI invoice processing automates the routing of invoice approvals and exceptions handling. ML algorithms identify the relevant stakeholders for approvals, based on contextual data from invoices and other data in approvals guidelines — for example, policies established by specific departments or contracts against which a vendor is billing. AI helps companies seamlessly orchestrate approval workflows by sending approvers real-time alerts and simple digital mechanisms for approval when their signoff is needed and by auto-approving invoices that already meet validation criteria. AI invoice processing also automates exception handling by flagging invoices with errors during the validation process and routing them to the right people to review and correct problems.

  5. Integration Into Accounting Systems

    AI invoice processing maps extracted invoice data to corresponding fields in accounting systems and automates the consistent entry of this data after it has been verified and validated. This automated data transfer streamlines the accounts payable workflow, reduces errors from manual entry and provides real-time visibility into invoice processing status and payment schedules across the organization. Integration of AI invoice processing with accounting systems also allows businesses to sync invoice data with the broader pool of financial data, making it easier to forecast future expenses and conduct financial planning based on real-time visibility.

  6. Continuous Learning With AI

    AI invoice processing systems come out of the box trained on common invoice formats and natural language models, but, like a fine wine, they get better over time because they’re designed to continually learn from the tasks they perform. For example, as data extraction errors are flagged during data verification and routed through an exceptions-handling process, an AI invoice processing system will learn and retain the correct way to process that particular type of data in the future. Additionally, as companies process new invoice templates, the AI-powered system will learn the necessary patterns to accommodate these formats.

Benefits of AI Invoice Processing

AI-powered invoice processing offers many compelling advantages for accounts payable departments. By leveraging cutting-edge technologies, businesses can dramatically accelerate invoice processing cycles, reduce labor costs associated with manual data entry and lower the incidence of errors that plague traditional methods. Below are the most common benefits associated with AI invoice processing.

  • Speeds up the invoice processing cycle: A recent study showed that one of the biggest challenges facing accounts payable departments is the length of time it takes to process and approve invoices. Analysis of best-in-class accounts payable departments that use automated AI invoice processing shows that these departments can reduce invoice processing time to 3.4 days, compared with 17.9 days for businesses without these practices.

  • Reduces labor costs: The average cost of labor to process a single invoice today is $10.18. By eliminating a significant amount of the manual work related to extracting data from invoices and entering it into financial systems, AI invoice processing can bring that average processing cost down by as much as 79%.

  • Lowers the incidence of errors in data extraction: Fat-finger blunders and other mistakes that come with the manual entry of invoice data can contribute to high error and exception rates in invoice processing. AI-powered automation of data extraction can reduce these errors and exception rates by more than half.

  • Ensures adherence to internal and external regulations: The automated data validation and verification processes enabled by AI invoice processing make it easier for businesses to ensure that vendors comply with internal accounts payable policies. Additionally, the consistency of automated data entry of invoicing data makes it easier to integrate everything into financial systems for external regulatory compliance purposes.

  • Handles increasing volumes of invoices: Manual interpretation and entry of invoice information doesn’t scale well. As a business grows and the accounts payable department is called upon to handle increasing volumes of invoices, AI invoice processing can ensure that teams keep up with the workload without needing to add personnel or experiencing delays in processing time.

  • Facilitates timely payments to vendors: Timely payment is crucial for healthy supplier relationship management. AI invoice processing speeds up processing times and approvals processes and makes sure that invoices are consistently input into financial and accounting systems so that vendors are paid on time and according to terms.

  • Provides valuable insights into spending patterns and supplier performance: Automatically capturing and extracting invoice data and routing that information into financial systems increases the amount of real-time information that can be used by financial analytics. This fuels valuable, real-time insights into spending patterns and supplier performance, which can be used to shape future strategies.

  • Detects and prevents fraudulent activities: One huge cost-avoidance benefit of AI invoice processing is that the technology is very good at spotting anomalous invoicing behavior that could indicate fraudulent activities. The verification processes and behavioral analytics of these systems automatically flag potential problems in invoices that can devolve into accounts payable fraud.

  • Resolves invoice discrepancies automatically: AI invoice processing systems identify discrepancies in invoices — such as a mismatch between invoice and PO amounts, incorrectly totaled line items or missing vendor information — and automatically route these exceptions to the appropriate stakeholders to quickly fix issues and keep the process moving forward.

  • Improves over time: The beauty of AI-powered invoice processing is that these systems are designed for self-learning. Automatic error analysis and model updates guarantee that models improve over time and that exception rates decrease accordingly.

Improve Your Invoice Processing With AI and NetSuite

With NetSuite invoice management software, powered by NetSuite Bill Capture , accounts payable departments will never again need to key in another invoice. Featuring AI/ML invoice capture and data extraction, the system scans invoices for relevant data, such as vendor name, PO number, items ordered, quantity and pricing; converts it all to digital text; and auto-populates billing records fields within NetSuite’s accounting software. These capture capabilities are paired with automatic two- or three-way matching of invoices against POs and receiving documents. Following verification, invoices route to appropriate personnel for review and approval, using workflows that can be tailored to a company’s requirements and approval hierarchy. The approval workflows automate reminders and provide approvers with a mobile-optimized app to simplify the process of approving each invoice.

Many of the biggest challenges facing accounts payable departments today center around the manually intensive work needed to process invoices and push them through the system. AI invoice processing can help resolve many of these issues. AI-powered automation speeds up invoice approval time, reduces the number of exceptions and errors that arise as invoices are entered into accounting systems and cuts the cost of processing invoices — especially as businesses grow and the volume of submitted invoices starts to accelerate.

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AI Invoice Processing FAQs

How can AI be used for invoice processing?

Artificial intelligence (AI) can be used for invoice processing by leveraging technologies, such as optical character recognition to extract data from invoices and machine learning to validate and categorize the extracted information for seamless integration into accounting systems. Additionally, natural language processing capabilities enable AI systems to comprehend and process unstructured text data included in invoices, such as descriptions and comments, further enhancing the automation and accuracy of the invoice processing workflow.

Can invoice processing be automated?

Yes, invoice processing can be automated through the use of artificial intelligence and other technologies, such as machine learning and natural language processing. These technologies facilitate automated invoice processing no matter what format a vendor uses to submit their invoices.

What is AI in billing?

Artificial intelligence (AI) is used in billing and accounts payable processes to automate invoice processing, validate billing data, speed up approvals processes and provide insights about expenses.

How do I create an automated invoice processing workflow?

Businesses can create an automated invoice processing workflow by using artificial intelligence technology that automatically captures text from invoices, extracts data from that text and inputs it into financial systems that route it through automated approval and exceptions-handling workflows.