One of the most time-consuming activities within cash application is the processing of paper remittance advice. Many companies, especially smaller players who have not switched to more recent systems, continue paying by paper check. A problematic side effect of this is that they continue to provide remittance as paper attachments to their checks. Reading and processing this information is a very time consuming, a low-value activity that occupies a significant chunk of time for cash application analysts. As a result, many companies have outsourced this task to their banking partners, who usually charge per keystroke. Because of the cost per keystroke, only key information is entered ? usually only payment header information. This means that there is still a lot of line-item detail that does not get processed. Invoices paid, amounts paid for each invoice, and deduction information are some of the details that usually get omitted and result in additional work being performed by cash analysts. Early solutions aimed to fully process the information in the remittance by combining Optical Character Recognition (OCR) with pre-defined templates. A user, administrator or business user, would tell the system where to extract each piece of information (payment amounts, invoice numbers, etc.), usually done by drawing rectangles on the scanned remittance image. However, this led to a new set of complications as a set of exact rules had to be followed when drawing these regions or the accuracy of data extraction would be seriously reduced. First-Generation Optical Character Recognition (OCR) Solutions faced a lot of issues. For example, the reading area rectangle had to be large enough to cover not just the payment amount on this check, but on checks with potentially larger amounts. For instance, the reading area rectangle should not be big enough to overlap fields where it can get confused by characters unrelated to the amount. Tables, such as those in situations where multiple invoices were paid with the same check, posed a different problem since the rectangle had to cover the entire length of the table to accommodate a variable number of invoices. On the flip side, any text, such as description and notes that were interspersed between invoice lines, would cause issues. The limitations of template-based solutions constrained their practicality and made it expensive to increase automation by creating templates for new payers. Recent technological advances have approached the problem from an entirely new direction. Artificial Intelligence and Machine Learning have addressed the problem regardless of whether the remittance is provided as paper, email, or in a customer portal. Such systems can be trained with historical remittance documents and the resulting extracted and parsed data. The systems then learn what keywords are used to identify key items. For example, the systems can learn over time that invoice, reference, and any other terminology encountered can be used to specify the invoices paid with the check. Another important piece of information that cash application analysts are very familiar with and apply instinctively is the location of information on the page. For example, the check number is most likely on top; the invoice numbers are towards the left side of the page, the amounts on the right, and the total amount at the bottom. Machine Learning and Artificial Intelligence (AI) are particularly effective when deployed in cloud solutions. The nature of a cloud system where a single AI entity is handling the processing of data from multiple payers across customers makes it easy and quick to learn and apply lessons learned across accounts and companies. The system is able to learn where to look for information and how to identify it. Once it identifies the information, validation rules can be run to ascertain accuracy. In a situation where the system has trouble extracting and identifying the critical fields, it will ask a user for guidance. The user will then teach the system in this peculiar situation and its learning will be applied next time a similar situation is encountered in remittance by this or other payers. There are numerous benefits of AI and Machine Learning and the intelligent cash application systems deployed on the cloud. At first, it helps reduce the cost to cash application teams and enables minimum delay in go-live.

“Automation rates achieved with AI solutions deployed on the cloud have been staggering ? More than 95% of invoices are processed and closed without any human intervention.”

ONLINE WEBINAR

Companies today are challenged with processing payments from a l alarge number of customers. In the absence of automation, this is a manual, repetitive effort that requires a large team to spend time on low-value, frustrating tasks. Learn how J&J Sales and Logistics Company Secured 95% Invoice Hit-Rate for their Cash Application operations. View Webinar Secondly, the automation rates achieved with AI solutions deployed on the cloud have been staggering. Oftentimes, customers are able to achieve 95% or more on-invoice automation hit rate. This means that more than 95% of invoices are processed and closed without any human intervention. Over time, as these higher systems learn, the automation rates continue to improve until only rare exceptions require human intervention. Without the need to define templates, the learning system can be up and running quickly and provide immediate results. This further provides value to the company as it minimizes the cost of expensive IT projects and the delays associated with procuring IT resources. AI and Machine Learning provide a new opportunity to streamline the use of resources within the credit or A/R department. These cash application automation solutions minimize barriers to adoption as they limit dependency on internal resources. They enable a team to support growth without adding additional resources to handle low-value activities and by realigning the team and shifting resources to higher-value activities such as research of deductions, analysis of accounts and credit reviews, and collections activities.