Machine Learning in Accounts Payable

Updated on Thursday 11th February 2021

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Large companies have many issued to deal with, however, payment transactions are among those posing most of these issues because of the financial losses they can encounter. Once such a problem appears, the entire process chain can be compromised as it needs thorough verifications in order to see where it first occurred.

The first department to check would have to be accounts payable, as this where all invoices are recorded, and payments are made. In order to address financial losses, Paid Analytix has created a machine learning accounts payable software that uses Artificial Intelligence to solve such problems in a very short time. Below, we invite you to read about it.

The main problems encountered by large companies

Most of the time, handling large volumes of invoices will leave room for accounting errors, especially when these rely on the human factor. This is why the most common accounts payable mistake is represented by double payments which further imply significant financial losses leaving the company in charge of trying to recover these amounts where possible.

In order to recover and then prevent future losses, Paid Analytix has created the machine learning accounts payable software which relies on the experience of large companies facing this exact problem.

Our software can first be used to perform an accounts payable audit followed by the verification of the invoices that lead to financial losses.

How does a machine learning accounts payable software work?

Our software uses Artificial Intelligence for its speed of providing fast and accurate results, while the machine learning part is used to do exactly that: learn what the user needs in order to provide customized solutions to the accounting issues found.

Our machine learning accounts payable software is a program that can be simply installed on a computer and after introducing the information requiring attention, it will generate a report with the issues found.

Common accounts payable mistakes found by our software

If we are to name the most common problems found during an accounts payable audit, we could divide them into:
  • 40% of the issues are often represented by wrong Invoice Number inputs,
  • 29% of them are generated by the Vendor Master File,
  • 16% are created by the multiple integration channels used alongside poor alert systems,
  • 11% of the invoices usually are dated wrongly,
  • 4% of the problems are generated by the amount on the invoice.

These errors make duplicate payments and false positives two of the most important issues addressed by our machine learning accounts payable software.

How to handle duplicate payment issues

When dealing with duplicate payments and their resolution, there are a few recommendations we can make. The first one would be to streamline the invoice acquisition channel by using fewer ways of receiving invoices. When they are sent via email, traditional post, or even courier, invoices can be entered into the accounting system several times. This usually leads to duplicate payments when the invoices must be paid.

In order to avoid such mistakes, you can create alerts with distinctive elements of a specific vendor and link them to the ERP system your company uses. These elements can also be entered in our machine learning accounts payable software which will recognize one or more of these elements much faster and thus alter you before a duplicate payment is made.

The Vendor Master File should be able to detect when a duplicate payment is about to be made, however, in some cases, it can be poorly maintained which is why problems appear. Our overpayment protection software, on the other hand, once it learns the particulars of each supplier, it will be able to detect errors much faster thus removing human effort from the process.

Machine learning accounts payable and invoice issues

Many duplicate payments have as root cause wrong invoice numbers and amounts. These can be simply addressed by our machine learning accounts payable software which is capable of detecting inconsistencies in a matter of minutes. Among these, we can mention the following:
  • our software can control the invoice before the payment is made,
  • it can also complete regular supplier reconciliation,
  • it can complete duplicate payment controls.

When using our software, these controls and verifications are complete quickly and effectively so you can make the payments in time and without having suppliers issuing the invoice once again.

If you have any questions about our machine learning accounts payable software and how you can use it for your company, we are at your service, so feel free to contact us anytime.

Radu FertoneaArticle by:
Radu Fertonea is the founder of Paid Analytix and Senior Manager with Executive MBA degree (Maastricht School of Management). He was director of operations at Societe Generale European Business Services, in charge with: Management of the Source to Report practice (Procurement, Accounts Payable, Accounts Receivable, Management Accounting) | Global redesign of end-to-end source to report activities | Consolidation of the Finance exercise for the European operations of Société Générale (100% coverage target by 2020) | Transition Management, Process Standardization and Continuous Improvement | People Management – team of 150+. With a broad experience in the financial field and after working with large societies which handle thousands of payments every year, came up with the idea which led to the creation of Paid Analytix.