Accounts Payable Machine Learning Use Cases

accounts payables machine learning use cases

The machine learning for accounts payable market is expected to grow from $6.1 million in 2016 to $76.8 million by 2021, at a compound annual growth rate (CAGR) of 53 percent. The software industry is rapidly embracing machine learning for account payable. As account payable becomes more automated, it also becomes more data-driven. Machine learning is enabling account payables stakeholders to leverage powerful new capabilities in this arena. In this blog post, you will learn machine learning / deep learning / AI use cases for account payable.

Key Business Processes for Accounts Payable

Here are the key business processes in relation to Accounts Payable:

  • Supplier onboarding: Collect basic information about each supplier or vendor. During this stage of the payables process, organizations collect essential data such as address and tax information. This is also an opportunity to collect more detailed information about each vendor, such as payment terms and business types, which can be used to negotiate contracts with vendors in the next stage of this process.
  • Maintain suppliers’ contract terms and conditions: This step of the payables process is also familiar to accounting professionals and it’s when they are required to ensure that all terms and conditions are properly documented, complete, and organized for reference at the future date. If something doesn’t sound right or feel right about any vendors then this stage of the payables process is where it’s recorded for later review.
  • Purchase order (PO) creation & amendment: Create purchase orders for all transactions involving new vendor agreements. The purchase order is essentially the first step of the procurement process, which can impact costs depending on how it’s structured and where it fits in with any relevant compliance requirements. In addition to creating purchase orders, organizations also use the purchase order system to amend or cancel existing orders.
  • Transaction approval/payment authorization: All finance professionals are familiar with the request to just approve this transaction so we can get it paid. The challenge is that these requests come infrequently, which means replying takes up a lot of time. An automated system would reduce the number of manual approvals required at this stage of the payables process and increase productivity.
  • Match suppliers with specific transactions: Account payables stakeholders have to process thousands of invoices every month, so it’s important for them to find those that match specific transactions quickly. It can be challenging for employees to keep track of all the possible invoices and figure out which ones need to be paid, especially because many vendors use unique invoice numbers or codes.
  • Reconcile suppliers’ invoices with purchases: This step of the payables process is familiar to anyone who works in an accounting department and it’s when each invoice submitted by a supplier is analyzed against transactions in the purchase order system, purchase requisition system, and so on before matching them up to ensure the numbers match up.
  • Payment processing: Process all transactions that have been approved for payment. At this stage of the payables process, organizations generate invoices, credit memos, or debit memos before making any payments to vendors. These documents are then submitted into an electronic workflow system for approval by authorized users. Once they’re approved, organizations can use the transaction processing system to make the payments.
  • Review or Amend suppliers’ account balances: The final step in the entire payables process is to review the balance owed by each vendor to ensure that it reflects any transactions that have occurred since the last time this information was collected. Once again, these changes are then submitted into an electronic workflow system for approval before they can be recorded in accounting systems where they’ll be used to generate any reports you might need for your own internal purposes.
  • Detect duplicate payments to suppliers/vendors: This step of the payables process is also familiar to accounting professionals and it’s when you’re required to identify any transactions that might have been paid twice, which can happen if a payment gets split between two different accounts or if someone manually corrects an entry in your system before entering it into another one.
  • Manage suppliers ratings and profiles: Organizations can use the information collected about their vendors in different stages of this process to generate reports that show which ones are more important than others in terms of how much they buy or sell or simply rate them on a scale to determine who’s acting in good faith when there are disagreements.
  • Evaluate suppliers for redundancies: This step of the payables process is also familiar to accounting professionals and it’s when they are required to look for any opportunities to get rid of unnecessary expenses. The idea is to reduce expenses without hurting the business.
  • Supplier fraud identification: The entire process of paying bills involves a lot of manual work which means it’s always at risk for human error. However, employees are also the first line of defense against fraudulent activity in your accounts payable department.

Machine learning techniques used for Accounts Payables

Machine learning techniques are being used in the ever-evolving world of accounts payables. Different machine learning models can be trained to address challenges across different business processes in accounts payables listed in the previous section.

Here are some of the key machine learning use cases for account payables:

  • Invoice matching for payment: Machine learning can be used to match invoices based on the description within the invoice itself, which in turn will determine whether or not payment should be made. OCR techniques can be implemented to read information from an invoice, such as its date, amount, customer ID, and so on. Invoice matching involves sorting through invoices to find the correct one, or correct group of invoices, for payment. Machine learning algorithms used for invoice matching include clustering algorithms, classification algorithms such as ensemble techniques, SVM, etc. Clustering involves grouping similar invoices together to determine if payments should be made. Classification models are used to classify whether payment should be made.
  • Matching suppliers for payment: Machine learning can be used to determine or rank the suppliers that should receive payment earlier others. The classification model will learn from historical invoice matching data within an account payable to understand which suppliers are likely to have invoices to be paid. Then, machine learning models can be trained to rank/sort these invoices to find the correct one, or set of invoices, to pay.
  • Fraudulent invoices: Machine learning models can be used to classify the invoices that are fraudulent, or potentially fraudulent. The classification model will learn from the account payable’s previous payment transactions to analyze if any invoice is suspicious. For example, an invoice might be flagged as a potential fraudster if it was paid by a certain customer in the past but is now being transferred to a different customer. Another example is related to classifying duplicate payment transactions. If an invoice has been paid before but is now being re-submitted, it could be a sign of fraudulent activity.
  • Supplier performance assessment: Machine learning models can be used to score the suppliers or vendors based on their invoices and related payment transactions. The machine learning model will learn from historical data, such as invoice amounts, late payments, etc., to forecast future supplier performances.
  • Redundant spend classification and supplier evaluation: Machine learning models can be used to classify the invoices that fall into certain categories, such as those that are frequently unpaid, those that have been paid frequently but should not be paid again, etc. The classification model will learn from historical transaction data within an account payable to understand which invoices should be placed into these categories.

Conclusion

Use cases for machine learning in accounts payable are increasing in number. There is a lot of manual work involved in account payables, and machine learning techniques can be used to make the process more efficient. It’s critical for your small business to stay competitive that you keep up with these trends by looking into machine learning possibilities for account payable procedures like invoice matching or fraud detection. We hope this blog post has helped you figure out how to get started training machine-learning models for account payable activities in your own company without spending too much time reading about it yourself. For assistance applying any of these machine-learning techniques to your company’s accounting processes, please get in touch with us right away.

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. I would love to connect with you on Linkedin and Twitter.
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