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How Automated Invoice Matching Saves Your Business Money and Time

Karthik Shiraly
·
November 10, 2021

Invoice matching is a critical prerequisite in the B2B payment process but it’s hampered by slow manual processes. Many accounting professionals have a strong desire to improve the process — as many as 43.8% would like automated matching implemented.

In this article, you’ll understand the basics of invoice matching, different ways of implementing it, and its importance to a business. You’ll learn about the challenges of doing it manually and the benefits of fully automating it. Finally, you’ll see how fully automated invoice matching works under the hood.

Introduction to Invoice Matching

Matching invoices is an important accounting task that compares the information in four types of documents to verify that they are all consistent with each other. The documents are:

  • Purchase order: The purchasing department first creates a purchase order (PO) containing the list of goods or services and the quantities it wants. They may also include acceptance criteria and any negotiated terms.
  • Invoice: After the vendor supplies the goods, they issue an invoice to the accounting department requesting payment and listing the goods or services they provided.
  • Goods-received note: In some companies, when the receiving department gets the goods sent by the supplier, they may issue a goods-received note or goods receipt.
  • Inspection report: In some industries, the receiving department may additionally inspect the goods before accepting them and reject goods that don’t meet some criteria.

When the accounting department receives a supplier invoice, the accounts payable (AP) team is responsible for matching invoice details against the details in the purchase order.

Some companies also match both against supporting documents like the goods receipt and inspection report, especially if the goods have high value or are supposed to meet stringent quality criteria.

Match Exceptions and Tolerances

If there’s a mismatch in the details of any two documents, it’s called a “match exception” or “deviation.” Mismatches can be of two types:

  • A price deviation is when an item and its quantity match but the prices don’t.
  • A quantity deviation is when an item and its price match but the quantities don’t.

In the real world, there may always be tiny discrepancies in price or quantity. Prices may deviate due to taxes or currency exchange rates. Quantities may deviate due to unexpected damage or transport mishaps. In a shipment of 1,000 items, if 995 are fine and five are damaged, it may not be a wise choice for a supply chain to reject all 1,000 items.

To cater to these realities and avoid payment delays, businesses define some tolerances. If the deviations are within those tolerances, the documents being compared are treated as a successful match, and payment is approved. If the deviations are above the tolerance levels, the invoice is put on hold and sent back to the vendor for replacement, clarification, or rectification.

Let’s explore the different ways these documents are matched.

Methods of Invoice Matching

Depending on your business practices, industry, and local laws, there are four ways to select and match these documents with each other.

2-Way Matching

2 way invoice matching

In two-way matching, the AP department verifies the vendor’s invoice against the corresponding purchase order. The items, prices, and quantities in the invoice should match those in the purchase order within acceptable tolerances.

Such PO matching is preferred when receipts and inspections are impractical or cost more than the goods themselves. It’s suitable for:

  • Recurring purchases
  • Small-value goods

3-Way Matching

what is three way invoice matching?

In three-way matching, accounts payable team compares the details in three separate documents:

  • The purchase order issued by the purchasing department
  • The goods received note issued by the purchasing department on receipt of goods
  • The vendor’s invoice

This is the most popular method in most industries.

4-Way Matching

Four-way matching is like three-way matching but includes a fourth document in the comparison: the inspection report by the accepting department. The accepting department inspects the goods for quality and conformity to requirements. Only accepted goods are cleared for payment.

This method is ideal for industries like automotive and manufacturing where quality control is critical.

Contract Matching

Some transactions, especially involving services, use contracts instead of purchase orders. The contract specifies the services, deliverables, metrics, and payment terms that have been agreed upon between the parties.

Because the information in a contract tends to be domain-specific and unstructured, it can’t be matched with an invoice mechanically. Instead, AP typically consults with the receiving department to understand the extent to which each term has been fulfilled and checks if the vendor has billed them correctly.

If the vendor has charged for a service that hasn’t been provided or fulfilled satisfactorily, the invoice is put on hold and sent back to the vendor for clarification.

Why Is Invoice Matching Necessary?

invoice information extraction

Invoice matching, when done accurately, benefits many of your business practices.

First, invoice matching is recommended by accounting standards like the Generally Accepted Accounting Practices and the International Financial Reporting Standards. Your business will be compliant with all financial auditing and reporting requirements.

Second, invoice matching helps you keep an eye on your bottom line and avoid financial losses from invoicing mistakes or fraud.

And finally, invoice matching reduces the risks of legal actions or penalties from vendors.

Problems With Manual Invoice Matching

information on cost per invoice

Invoice matching remains a largely manual process because until recently, automated approaches have not been very reliable or accurate. Older automated approaches have problems understanding the wide variety of layouts and information in accounting documents like invoices and purchase orders. Companies are forced to rely on manual matching.

Unfortunately, doing it manually has challenges that cancel out many of the benefits of invoice matching:

  • Highly labor-intensive: Manual invoice matching requires concentration and attention to detail. To speed things up, companies have to employ more skilled people. Compared to 2-way matching, the problem is exponentially worse in 3-way and 4-way matching because of the inherent difficulties in coordinating between multiple departments.
  • More expensive: Hiring, employing, and training more employees increases the total cost of the workforce. It also pushes up the cost of processing one invoice to $15-$40.
  • Time-consuming: Accurately matching just one invoice manually takes a lot of time. One survey estimates that the invoice approval process takes more than a week in 45% of cases and requires five or more people.
  • Error-prone: Manual involvement makes invoice matching prone to errors. That’s why companies are forced to employ more people and conduct multiple rounds of reviews.  
  • Susceptible to fraud: Vendor fraud may get overlooked especially if it’s spread out over time and multiple invoices. The blowback of fraudulent invoices can be massive.
  • Create problems during audits: The slowness and errors can result in difficult questions during financial audits and possible legal actions against the company or its employees.
  • Lacks transparency in expenses: Management can’t get the real-time and accurate picture of expenses it needs for decision-making.
  • Delays payments: The slowness and unreliability of manual matching can result in payment delays.
  • Damages vendor relationships: Payment delays, coordination delays, misunderstandings, and disagreements during invoice matching can damage the relationships a company has with its suppliers.

Fully Automated Invoice Matching

Fully automated invoice matching combines deep learning, computer vision, and natural language processing to understand invoices and other documents the same way people do regardless of variations in layouts, terminologies, languages, or other aspects.

Older semi-automated workflows involve some manual steps like creating invoice templates for data extraction, entering data in spreadsheets before the automated steps take over, or verifying the extracted data after the automated steps.

Unlike these workflows, fully automated invoice matching is done by machines with minimal manual intervention. Let’s understand all the benefits of such AP automation.

8 Business Benefits of Fully Automated Invoice Matching

width.ai invoice processing information
Fully automated invoice matching brings enormous advantages to your procurement and accounting processes.

1. Deep Understanding of the Documents

Fully automated invoice matching replicates the deep semantic understanding of documents that people have. This allows accurate semantic matching of information across documents if there are variations in headings, words, terminologies, languages, currency formats, or date formats. 

For example, if there are special conditions in both the purchase order and invoice, it can interpret those conditions just like people can and check if the items in an invoice match those conditions.

2. Fast and Scalable

Even 4-way matching can be done in a matter of seconds compared to the hours (or even days!) that manual matching requires. The entire process is highly scalable because it’s automated end-to-end and supports millions of invoices per day.

3. Lower Costs

Fully automated invoice matching lowers both your capital and operating expenditures. The capital expenditure on the invoice matching software and infrastructure is a lot lower than that required for hiring and training people. The operating expenditure on cloud infrastructure is far lower than ongoing employment costs. The cost per invoice also reduces by about 75%.

4. High Accuracy

invoice processing with deep learning vs templates

The accuracies of both the data extraction process and the semantic matching of information across documents are extremely high compared to both manual and older semi-automated approaches.

5. Reliably Detects Fraud

Unlike people, fully automated invoice matching can sift through all your historical invoices in seconds. This enables it to detect overpayments, duplicate payments, and other kinds of vendor fraud that are spread out over time.

Aside from vendor fraud, some medium and large businesses face insider fraud where employees collude with vendors for mutual benefits. Dishonest employees may cooperate with fraudulent vendors to tamper with purchase orders, hide overpayments, or allow duplicate payments. Fully automated invoice matching helps detect them.

6. Streamlines Audits

The reliability and accuracy of the matching bestow a high degree of confidence in its results. Your financial audits are guaranteed to go far more smoothly with such accurate results.

7. Supports Real-Time Business Intelligence

The ability of fully automated invoice matching to complete even 4-way matching within seconds enables your business intelligence teams to get accurate cash flow and expense data in real-time.

8. Improves Vendor Relationships

A fast, accurate, and deep understanding of invoices enables you to avoid payment delays and fulfill all payment criteria of vendors. You may even receive early payment discounts from vendors. Accounts payable invoice matching strengthens your relationships with your suppliers.

How the Fully Automated Invoice Matching Process Works

invoice to json processing

Let’s understand some of the state-of-the-art techniques under the hood of fully automated invoice matching.

1. Data Extraction From Invoices, POs, Receipts, and Inspection Reports

The first step involves accurate, deep, and semantic data extraction from all the relevant documents like invoices, purchase orders, goods received notes, and inspection reports. 

This is achieved by combining deep learning, large language models, and text recognition models to identify and understand both printed and handwritten text just like people do. 

Regardless of layout or other aspects, all the data in these documents are converted to structured data consisting of fields and their values. For example:

  • Purchase order details in the rows are identified, extracted, and labeled to produce product names and their requested quantities.
  • The line items in a vendor invoice are again identified and labeled to give product names, unit prices, quantities, total invoice amounts, and other invoice data.
  • Payment criteria and due dates are identified and labeled.
  • Acceptance and rejection remarks in inspection reports are identified and noted.
  • All data validations are done using suitable machine learning models.
  • The invoice processing system then routes the extracted data to other software systems like the enterprise resource planning system. The ERP system is where the complete accounting status is stored.

2. Line Items Extraction

Fully automated invoice matching is capable of identifying tables, columns, rows, and cells across multi-page documents. Each invoice line item in a table is extracted and labeled based on its respective column.

3. Product/Service Title Matching

Matching the products or services across invoices, purchase orders, and other documents can get complicated because:

  • The item names and descriptions used by the purchasing party and the vendor may be different. They may contain typos or use different languages.
  • They may contain complex information like universal product codes (UPCs) and stock-keeping units.
  • The items in an invoice may be listed in a different order from those in its purchase order.

Fully automated invoice matching uses intelligent data matching based on deep learning and natural language processing to solve all these challenges. Product titles, descriptions, and data like SKU numbers are matched intelligently using state-of-the-art deep large language models like GPT-3 to achieve human-level understanding.

4. Quantity and Price Matching

Quantities and prices are matched regardless of currency and number localization. The tolerance levels for matching can be checked through price outlier detection techniques.

5. Payment and Contract Terms Matching

Payment and contract terms are typically written in natural language sentences that don’t conform to any strict syntax. While they can be very challenging for semi-automated approaches, fully automated invoice matching extracts and understands them effortlessly using large language models.

Width.ai’s Invoice Matching Automation Solution

width.ai invoice processing

In this article, you saw how invoice matching benefits your business but how manual matching cancels some of those benefits. By using Width.ai’s fully automated invoice processing solution, you can process millions of invoices, matching them within seconds. Contact us to see a demo of our touchless invoice processing.