Reduced Duplicate Invoices in Accounts Payable with Machine Learning
In recent times, Machine Learning has become a buzzword and more and more businesses are harnessing their profits and productivity by applying Machine Learning ideas. Its applications are very diverse and one such application of Machine Learning is the detection of Duplicate Invoices in Oracle Accounts Payable to prevent duplicate payment of invoices.
Why Duplicate Payment of Invoices is a Problem that needs to be addressed?
Duplicate invoices are not only a headache for the Accounts Payable team, but they can cause a significant financial loss per month too. There can be multiple reasons behind duplicate invoices but the most probable one is that businesses receive invoices through multiple channels i.e. email, online portals, or XML files, etc. Vendors send the same invoice through multiple channels to make sure it has been delivered to the company. The duplicate payment of invoices is not a new problem. It has existed for years now. Research by Rutgers University revealed that in 1998, Department of Health and Human Services estimated that Medicare paid $89 million in duplicate invoice payment cases.
Imagine for large companies the chances of the duplication cases increase manifold and hence the lost amount. It not only leads to increased cost of invoice processing but in unfortunate cases leads to duplicate payments too. Other than that, the existing manual invoice review processes to detect duplicate invoices couldn’t effectively review all Accounts Payable invoices.
Machine Learning “Panacea” for all your Duplicate Invoices Problems?
Sending on-time and correct payments is a big issue for businesses especially in the time when corporate fraud is increasing with each passing day. The application of Machine Learning technology can not only help businesses to detect duplicate invoices but also help to streamline the accounts payable through Process Automation. Let’s dwell on the use cases of Machine Learning in the Accounts Payable sector:
- Detection of Duplicate Invoices:
One of the biggest applications of Machine Learning in Accounts Payable is the detection of duplicate invoices. Powered by Machine Learning the duplicate invoices are automatically detected based on multiple invoice attributes like the invoice number, date, amount, bank account and company. After reading invoices data from Oracle, a model can select all the potential duplicate invoices thereby eliminating the accidental OCR or human errors and saving unnecessary processing costs and avoid duplicate payments. - Categorization of Accounts Payable invoices:
Machine Learning models are intelligent and have the capability of self-learning too. They are constantly training themselves by finding patterns in the data set. Due to this pattern identification, a Machine Learning model can categorize invoices in various categories i.e. the invoices that have a high chance of being paid late, on time or too early. - Matching Invoices with Purchase Orders:
One of the benefits of using Machine Learning in Invoice Processing is that it can match the invoice data with the purchase orders to ensure that the invoice is matched with the correct purchase order. - Eliminating the Accounts Payable Fraud Incidents:
Machine learning enables end-to-end automation of business cases. Instead of following static workflows, ML models learn the Accounts Payable processes, frequency of invoices from a certain vendor and can detect when an anomaly occurs in invoice processing. - Assessment of Supplier Performance based on historical data:
Machine learning models can not only automate and streamline your process but also identify the patterns from invoice payments based on Invoices and associated data. ML models can learn from historical data and forecast about a specific vendor i.e. whether the vendor will cause a delay in supplies etc.
How Process Mining in Celonis adds value to the Duplicate Invoice detection process:
- Automatic Machine Learning integration: Celonis can help businesses to eliminate discrepancies from the business processes and set a standard process across the organization. It will reduce the chances of error and speed up the execution of Accounts Payable processes by making sure that the newly entered invoices are checked on time by the ML model.
- Action Engine integration to automate activities: When a new duplicate invoice is detected by Celonis the Action Engine ensures that the right people are notified so that they can check the invoice. Once it is confirmed that the invoice is indeed duplicate it can immediately be cancelled so the duplicate payment can be prevented. The earlier a duplicate invoice is detected the lower the cost are to eliminate it and to prevent duplicate payment.
- Use new data to improve the Machine Learning model: In Celonis, all activities before and after the duplicate invoice is identified are available and can be analyzed. These activities provide new data to improve the Machine Learning model to detect duplicate invoices.
Closing Thoughts:
In a nutshell, the business sector is expanding day by day. With the expansion of businesses, data generation is also multiplying. Earlier businesses do not have the means and way to exploit the benefits of data. But, with the advent of Machine Learning powered products, businesses can streamline, optimize, and automate their business processes and increase profits manifold. In the case of thousands of Accounts Payable invoices, human intervention is limited and error-prone. Therefore, by employing Machine learning solutions in Celonis, detection of duplicate invoices in accounts payable can be made fast, accurate, and free from fraud. Now is the right time to invest in Machine Learning for long-term business profits. Get in touch now for more information about eliminating duplicate invoices in Oracle AP with Celonis!