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Invoice Processing
Mar 16, 2023
Min Read

Unlock the Power of AI in Invoice Matching

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Chief AI for Everyone Officer

Invoice matching plays a crucial role in the accounts payable process, ensuring that businesses only pay for the goods and services they've actually received. As companies grow and the volume of invoices increases, manual invoice matching can become a time-consuming and error-prone task. Automating this process not only streamlines operations but also improves accuracy, leading to cost savings and better financial management. In this article, we'll explore the concept of invoice matching, its different methods, and how the super.AI Intelligent Document Processing (IDP) solution can revolutionize this essential financial process.

What is invoice matching?

Invoice matching is a critical component of the accounts payable process, involving the comparison of an invoice to its corresponding purchase orders, receipts, or contracts. The primary purpose of invoice matching is to verify the accuracy of the invoice before making a payment, ensuring that the details such as quantities, prices, and descriptions of goods or services match the initial agreement.

This process helps businesses maintain control over their expenses, identify discrepancies, and avoid overpayments or paying for goods and services that have not been delivered. By implementing effective invoice matching, organizations can enhance their financial management, minimize the risk of fraud, and improve relationships with suppliers through timely and accurate payments.

Different ways of invoice matching

There are several methods of invoice matching, each with its own level of complexity and thoroughness. The most common types of invoice matching include two-way, three-way, and four-way matching. Let's take a closer look at each method:

2-Way Matching

Two-way matching is the simplest form of invoice matching. In this process, the invoice is compared against the purchase order to ensure that the details, such as product or service descriptions, quantities, and prices, are consistent between the two documents. This method provides a basic level of verification, making it suitable for smaller businesses or less complex transactions.

3-Way Matching

Three-way matching is a more comprehensive process that involves comparing the invoice to both the purchase order and the goods receipt. This method adds an extra layer of verification by checking whether the goods or services have been received as stated in the purchase order before processing the payment. Three-way matching is commonly used by medium and large-sized businesses to improve the accuracy of their accounts payable process.

4-Way Matching

Four-way matching is the most rigorous method of invoice matching. In addition to comparing the invoice with the purchase order and the goods receipt, this process also involves reviewing the inspection or quality report to ensure that the delivered goods or services meet the agreed-upon quality standards. This method is particularly useful for businesses dealing with high-value or mission-critical products and services, where maintaining quality is of utmost importance.

Each of these methods offers different levels of verification and is suitable for varying business needs. As companies scale and their transactions become more complex, adopting more rigorous invoice matching methods can lead to improved financial management and cost control.

Automating invoice matching

As businesses grow and the volume of invoices they need to process increases, manual invoice matching can become a cumbersome and error-prone task. Beyond this, research on the pain points of the AP department shows that invoice matching is the fourth largest challenge in the entire invoice management process.

Automating the invoice matching process can significantly streamline operations, reduce errors, and provide numerous benefits:

Streamlined Workflows

Automation eliminates manual data entry and comparison, making the invoice matching process significantly faster. As a result, businesses can process higher volumes of invoices in less time, freeing up resources for more strategic tasks.

Greater Precision

Automating invoice matching reduces the risk of human error and increases the accuracy of financial records. This improved precision helps to minimize discrepancies in accounts payable and fosters better financial management.

Enhanced Data Quality

By employing automated invoice matching, businesses can ensure that their financial records are accurate, up-to-date, and reliable. This high-quality data enables organizations to make more informed decisions, optimize their supply chain, and identify cost-saving opportunities.

As more organizations recognize the advantages of automating invoice matching, technology solutions, such as Intelligent Document Processing (IDP), are emerging to meet this need. In the next section, we will discuss how super.AI IDP can be used to automate the invoice matching process.

Super.AI IDP for invoice matching

The super.AI Intelligent Document Processing (IDP) solution offers a powerful way to automate invoice matching, significantly improving the efficiency and accuracy of the accounts payable process. By utilizing advanced data extraction techniques, super.AI IDP can process complex documents and leverage the field-level database lookup feature for invoice matching.

Field-level database lookup

The field-level database lookup feature allows users to link a database to specific fields in the General Document Processor (GDP) data program, enabling the extracted data to be matched with values in the database. Here's how to set up the database connection and configure matching settings:

  1. Create a new field in GDP and select the data type "Calculated."
  2. Enter the data set URI. This is the location of the database you want to link to GDP. This can be a public URL or a data:// link connecting to your super.AI storage.
  3. Define which fields to match against the data in the dataset. Select the fields in GDP that you want to compare with the values in the database.
  4. Define the number of candidates to present for human review. This is the number of matches that will be returned for interactive selection. If set to 0, matching will be disabled.
  5. Define the matching threshold. This is the percentage of fields that should match for automatic assignment. For example, a threshold of 0.8 means that 80% of the fields must match for the value to be automatically assigned.
  6. Define the Output Column Name. This is the name of the column in the output.
  7. Click "Setup" to link the database to GDP and begin matching values.

Once the fields are set up, GDP will look up values in the database and match them to the extracted data from your documents. If the matching threshold is met, the corresponding value will be automatically assigned to the output field. If there are multiple matches found, the number of candidates as defined will be shown for human review, and users can select the correct one.

By setting up the matching threshold, you can control the level of confidence in automatic matching. Lowering the threshold would increase the chance of getting matched but also raise the risk of mismatches, while raising the threshold reduces the chance of mismatches but also decreases the likelihood of getting matched.

The field-level database lookup feature is particularly useful for businesses that need to extract specific values from their documents and ensure that the extracted data matches one of those values. This functionality can improve extraction quality and automate the invoice matching process to a significant extent.

Benefits of automated invoice matching with super.AI IDP

Automating invoice matching with the super.AI IDP solution provides businesses with a range of advantages that enhance the accounts payable process. By utilizing the field-level database lookup feature for invoice matching, users can experience the following benefits:

Faster processing times

Automation speeds up the invoice matching process, allowing businesses to process a higher volume of invoices in less time. This increased efficiency translates to faster payments and improved cash flow management, leading to better supplier relationships and a more agile financial operation.

Reduced errors

Manual invoice matching is susceptible to human error, which can result in overpayments, underpayments, and duplicate payments. Automating the process with super.AI IDP significantly reduces the likelihood of errors, ensuring more accurate financial records and minimizing the need for time-consuming corrections.

Lower costs

By streamlining the invoice matching process and reducing errors, businesses can achieve substantial cost savings. These savings may stem from decreased labor costs, reduced overpayments, and minimized penalties for late payments, all of which contribute to improved financial performance.

Enhanced compliance and fraud prevention

Automated invoice matching can help businesses maintain compliance with internal policies and external regulations by providing a clear, auditable record of the process. Additionally, the automated system can identify discrepancies and potential fraud, alerting businesses to issues that may otherwise go undetected.


As businesses grow and the volume of invoices increases, the super.AI IDP solution can easily scale to accommodate their evolving needs. This flexibility ensures that companies can continue to benefit from efficient and accurate invoice matching, even as their operations expand.

Improved Decision-Making

The accurate and efficient processing of invoices provided by super.AI IDP allows businesses to maintain up-to-date financial records. This reliable data enables better decision-making, empowering organizations to identify cost-saving opportunities, optimize their supply chain, and make more informed financial decisions.

By automating invoice matching with the super.AI IDP solution, businesses can unlock a range of benefits that lead to more efficient, accurate, and cost-effective financial management.

Common invoice matching software integrations

To maximize the benefits of automated invoice matching, businesses often integrate super.AI IDP with various other software solutions used in their financial operations. These integrations can help streamline workflows, enhance data exchange, and ensure seamless communication between different systems. Some common software integrations for invoice matching include:

Enterprise Resource Planning (ERP) Systems

Integrating super.AI IDP with ERP systems like SAP, Oracle, or Microsoft Dynamics enables businesses to synchronize invoice data with their broader financial and operational management systems. This seamless connection ensures that accounts payable data is accurately reflected across the entire organization, simplifying reporting and improving overall financial management.

Accounting Software

By connecting super.AI IDP to accounting software like QuickBooks, Xero, or Sage, businesses can automatically import matched invoice data into their accounting systems. This integration simplifies the process of reconciling invoices with the general ledger, reducing manual data entry and increasing the accuracy of financial records.

Procurement Software

Linking super.AI IDP with procurement software, such as Coupa or Ariba, allows businesses to streamline the purchasing process and enhance visibility into their supplier relationships. Automated invoice matching ensures that invoices are accurately matched to purchase orders and receipts, making it easier to manage procurement activities and monitor supplier performance.

Document Management Systems (DMS)

Integrating super.AI IDP with document management systems like SharePoint or M-Files helps businesses manage their invoice documents more effectively. By automating the extraction and matching of invoice data, businesses can ensure that their digital document repositories are up-to-date and organized, improving record-keeping and simplifying the retrieval of relevant information.

Business Intelligence (BI) Tools

Connecting super.AI IDP with BI tools like Tableau, Power BI, or Looker allows businesses to analyze their accounts payable data more effectively. By incorporating accurate invoice matching data into their analytics workflows, organizations can gain valuable insights into their financial performance, identify potential cost savings, and make more informed business decisions.

These integrations demonstrate the versatility of the super.AI IDP solution for invoice matching, as it can be easily connected to various software systems to create a comprehensive and efficient financial management ecosystem. By leveraging these integrations, businesses can further optimize their accounts payable processes and enjoy the full benefits of automated invoice matching.

Embracing the future of invoice matching

The ever-evolving landscape of financial management demands innovative solutions that can keep pace with business growth and increasing complexity. By leveraging the power of super.AI IDP for invoice matching, organizations can enjoy a more efficient, accurate, and cost-effective approach to managing their accounts payable processes.

Automating invoice matching not only reduces the burden of manual data entry and comparison but also enhances the accuracy of financial records and provides valuable insights to inform business decisions. With seamless integration into various software systems, super.AI IDP offers a flexible and scalable solution for businesses of all sizes.

By embracing the future of invoice matching with super.AI IDP, organizations can unlock the full potential of their financial operations and secure a competitive edge in an increasingly data-driven world.

Other Tags:
Invoice Processing
Accounts Payable
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