Unleashing growth opportunities in AP automation- Data and Machine Learning

Most corporate individuals are under the impression that corporate assets stand for a fixed set of commodities. These commodities include the latest computers and laptops, buildings, and printers. However, a unique corporate asset may not have come to your mind, and it is an asset that is slowly becoming valuable. It’s “Data.” With the impressive advancement in technology and science, data is becoming a highly-valued corporate asset.  Data and machine learning in invoicing is transforming accounts payable today.

Data and machine learning in AP automation

Data is a strategically essential differentiator. The increasing value of data provides finance professionals with a fantastic opportunity. The data they can gather in the form of invoices, payments, cash flow, etc., can be utilized effectively.

How? It’s simple.

The information gathered from these different data categories is extremely useful, and data can generate valuable insights to facilitate the implementation of intelligent business decisions.

So, what are some of the intelligent business decisions possible?

The valuable insights gathered from the obtained data can be used for curating personalized services. These personalized services include an invoice, payment, and customized offers to suppliers, vendors, and customers. Customized services using data can get delivered in faster and more economical ways.

Besides that, data in digital format enables better utilization to automate invoice processing that saves costs and improves productivity. One can download invoices automatically from portals or integrate your banks to map invoice data to banking transactions.

What is the role of machine learning in AP automation?

Now let’s talk about how accounts payable system personnel can benefit from this increasing value of data. Employees working in the accounts payable department can learn and become more aware of machine learning. Machine learning is a data-centric artificial intelligence technology. Data and machine learning in invoicing delivers intelligent business insights.

Here is how machine learning can help in payment processing:

• Opting for automated AP processes using machine learning involves analyzing and processing data to identify and predict supplier payment-related behavioral patterns. Apart from supplier payment patterns, invoicing patterns can also be detected and predicted.
• Analysis of behavioral data through machine learning can also help identify the checks that do not arrive on time or at all. This information is imperative to the smooth functioning of businesses. Machine learning can then get used to convey the information that something is wrong and requires improvement.
• If notification regarding the delay in receiving checks is received, AP software can highlight or implement intelligent business decisions such as offering more suitable payment options to the supplier.
• In AP automation, machine learning can also play an all-important role in enabling companies to predict market and vendor payment risks.
• Future spending spikes and falls can also get forecasted through machine learning.
• Machine learning can calculate the statistical probability of the occurrence of invoice anomalies.
• Software providers can also utilize machine learning to identify unusual payment behavior. Machine learning allows the software provider to alert the supplier about such discrepancies. Machine learning enables the supplier to be alert and take the necessary action sooner with invaluable data insights.

Today with AP automation software using the latest capabilities of artificial intelligence and machine learning, organizations can gain valuable insights through data. Machine learning is the key to unlock the valuable information, patterns, and trends about your invoices, payments, and accounting data can generate.

Accounts payable automation has great potential, and it is advisable to take early steps into AP automation. Discover GetMyInvoices – Sign up for a free trial.