Invoice Processing Machine Learning Use Cases

Invoice processing is a critical part of any business. It’s the process of creating, managing, and paying invoices. Without invoice processing, businesses would have a difficult time keeping track of their finances. There are many different invoice processing use cases. For example, businesses can use invoice processing to keep track of customer payments, manage vendor contracts, and streamline their accounting processes. Invoice processing can also be used to detect fraud and prevent errors. Machine learning / AI can be used to improve invoice processing in a number of ways. As a product manager, it will be helpful to understand these use cases and how machine learning can be used to improve invoice processing. In this blog post, we will discuss some of the ways that invoice processing can be improved through machine learning. We will also provide some examples of how invoice processing can be used in different industries.

What is invoice processing and what are key challenges?

Invoice processing is the process of reviewing and approving invoices for payment. Invoices can be generated manually or electronically, and they typically include detailed information about the goods or services purchased, the quantity and price of each item, the date of purchase, and the terms of payment.  Invoice processing can also be defined as the process of verifying and converting paper invoices into digital format. There are a number of benefits to digitizing invoices, including improved accuracy, faster processing times, and reduced storage costs. To start the invoice processing procedure, businesses first need to scan their invoices into a digital format. Once the invoices are digital, businesses can then use optical character recognition (OCR) software to extract data from the invoices. This data can then be fed into an accounting or ERP system for further processing.

Invoice processing is a very important process for account receivables & account payables department in any organization. By definition, accounts receivable is the money owed to a company by its customers for goods or services that have been provided. Invoices are the documentation of this, and as such, processing them in a timely and efficient manner is essential to ensuring that account receivables are properly managed. Invoice processing is a critical part of account receivables. Without accurate and timely Invoice processing, businesses would have difficulty knowing what was owed to them, when payments were due, and ultimately collecting on their receivables. Invoice processing begins with the creation of an Invoice, which is then sent to the customer. The customer has a certain period of time to pay the Invoice, after which they are considered delinquent. To avoid deliquent accounts, it is important for businesses to have a efficient Invoice processing system in place. 

Common challenges in Invoice processing include incorrect or missing data, duplicate Invoices, and incorrect payment terms. Here are further details:

• Incorrect or missing data: Invoices contain critical information such as payment deadlines and amounts, and any errors can lead to late payments, inflated costs, and other problems. The traditional method of manual data entry is error-prone and time-consuming, and it is becoming increasingly impractical as businesses deal with larger volumes of invoices.
• OCR errors: Invoice processing is the task of extracting data from invoices, so that the information can be used for bookkeeping and other purposes. This is typically done using optical character recognition (OCR) software, which can read and interpret text from images. However, OCR can be inaccurate, especially if the invoice data is damaged or poorly formatted.
• Processing large volume of invoices: One of the challenge related to invoice processing is processing large voluminous invoice. This is a time-consuming task and often requires businesses to hire temporary staff to help with the workload. In addition, businesses need to invest in storage space to keep all of their invoices safe and organized.

Invoice automation driven by AI & machine learning can help to address the above challenges by providing a way to capture Invoice data electronically and route Invoices for approval automatically. 

Invoice processing & machine learning

The following are some use cases related to invoice processing that can be tackled using machine learning / AI based solutions:

• Categorization of Invoices: Invoice categorization using machine learning is one of the ways to deal with the challenge of processing large volumes of invoices. Machine learning algorithms can be used to automatically categorize invoices based on invoice data, such as vendor name, invoice amount, and invoice date. This can help businesses to quickly identify and process invoices that are due for payment.
• Duplicate invoices detection: Duplicate detection is another common use case for machine learning in invoice processing. Duplicate invoices can occur when businesses receive multiple copies of the same invoice, or when an invoice is processed more than once. Machine learning algorithms can be used to detect duplicate invoices by comparing features such as invoice number, vendor name, and invoice amount. This can help businesses to avoid paying the same invoice twice.
• Extracting invoice data: One of the most common use cases for machine learning in invoice processing is extracting data from invoices. This can be used to automate the task of data entry, and to improve the accuracy of invoice data. Machine learning algorithms can be used to extract data from invoices by reading and interpreting the text on the invoice. This can help businesses to quickly and accurately capture invoice data, without the need for manual data entry. OCR technology plays an important role in extracting data from invoices. However, extracting data using OCR comes with its own set of challenges due to error-prone OCR. This is where a custom machine learning solutions can be created by capturing data using advanced cloud technologies such as AWS Textract (AWS) or CNN-based models to capture data from invoice images. CNN based models can be used to capture data from invoice images with a high degree of accuracy. Amazon Textract is Amazon Web Services’ (AWS) optical character recognition (OCR) service. Amazon Textract can be used to automatically extract text and data from documents, such as scanned paper documents, PDFs, and images. Amazon Textract goes beyond simple OCR to also identify the contents of fields in forms and information stored in tables.
• Approving invoices: Another common use case for machine learning in invoice processing is approving invoices. This can be used to automatically approve or reject invoices based on invoice data, such as vendor name, invoice amount, and invoice date. Classification models can be trained for this purpose using invoice data. This can help businesses to quickly and accurately approve or reject invoices, without the need for manual review. That said, an exception can be raised for manual handling if machine learning models are unable to predict the approval or rejection of invoices with high confidence.
• Predicting invoice payment: Another use case for machine learning in invoice processing is predicting invoice payment. This can be used to predict when an invoice is likely to be paid, based on invoice data, such as vendor name, invoice amount, and invoice date. Regression models can be trained for this purpose using invoice data. This can help businesses to better manage their cash flow by predicting when invoices are likely to be paid.
• Matching invoices to payments: One of the important use case for invoice processing and account receivables is matching invoices to payments. This can be used to automatically match invoices to payments, and vice versa. Machine learning algorithms can be used to match invoices to payments by comparing features such as invoice number, vendor name, invoice amount, and payment date. This can help businesses to quickly and accurately reconcile their accounts receivable.
• Automatically generating invoices from customer purchase data
• Automatically routing invoices for approval

The above are some examples of how machine learning can be used in invoice processing. Machine learning can help businesses to automate the task of data entry, improve the accuracy of invoice data, and speed up the invoice approval process. In addition, machine learning can be used to categorize invoices and detect duplicate invoices. These use cases can help businesses to save time and money by automating the invoice processing task. However, it is important to note that machine learning is not a silver bullet solution for all invoice processing problems. In case, you would like to suggest some more use cases of machine learning in invoice processing or have any questions, feel free to drop a message. Thank you.