3 Steps To Measuring Effectiveness Of OCR And Cash Application Automation

Jay Tchakarov

I hear more and more questions about how to measure performance of optical character recognition (OCR) and cash application automation. No surprise there – every vendor is pushing different metrics to put their own technologies in the best possible light. Many of these metrics, unfortunately, do not paint a clear picture (intentionally, perhaps?). When measuring performance, it is important to know exactly what you are looking to achieve.

One common metric that keeps coming up is the kill rate, or the percentage of invoices the accounting system or ERP side have successfully closed with the output of the cash application system. The problem with this metric is that it is not a measure of automation but of accuracy. It provides only a partial picture since it combines both the automated processes and the manual part that the analysts do to fix exceptions and to handle non-automated payments. Therefore, if you are looking for insight into how much your team is saving or how much more efficient cash application automation has made them, these metrics are not going to help you. Instead, take a look at these three metrics:

1. Measure real automation – how much of the work is done by the system alone, without your team’s involvement?

For a measure of actual automation, consider how much of the work is done by the system itself without being touched by your analysts. This is the fraction of all invoices and payments that are processed and applied accurately without analyst intervention or time taken – no exceptions, no double-checking. This metric will tell you how much of the work is taken up by the system and how much automation is saving you.

2. Measure total throughput to verify how the system is making your team more efficient.

If you are looking to see how the team is functioning overall and performing in the presence of an automation system, you consider metrics such as throughput or overall checks or payments processed per team member per day. These two metrics provide an accurate picture of how much automation you are getting as well as how this automation is helping your team become more efficient.

3. Measure effectiveness of automation for specific types of payments and remittances to get insights into opportunities for improvement.

The above metrics will provide you with a measure of automation and efficiency. However, to identify opportunities for improvement, you need to drill down a bit deeper. Consider monitoring automation percentage and throughput by payment or remittance type. This will tell you whether the system and your team work better with certain remittance types than others and will pinpoint opportunities for improvement and additional automation. How are you handling paper remittance? Email? Portals? Is there anything that is slow and manual that technology has not solved yet? If your cash application vendor provides a holistic automation solution that works with all remittances (paper, email, electronic data interchange, Web portals, etc.), you will be able to extend the automation and realize additional savings.

Measuring performance of a cash application automation system is easier than you think. Automation is a great promise but, as with all technology, it is important to not approach it blindly but to measure and continuously improve. Start with a measure of how many payments and invoices are never touched by your team – this is the real measure of automation. Enhance it with overall team throughput. This will show you how much you are saving from the automation and how it is impacting your team. Drill down to specific payment types, remittance formats, and customers to identify resource bottlenecks and further opportunities for improvement.

For more information about cash application automation, visit HighRadius.

This article is republished by permission from HighRadius.


Jay Tchakarov

About Jay Tchakarov

Jay Tchakarov is vice president of Product Management and Marketing at HighRadius Corporation. As part of HighRadius’ executive team, he is responsible for defining HighRadius’ Credit and A/R products and for educating the market about the value of automation and advanced technologies. He and his team work closely with sales, consultants, and customers to make sure the products address critical pain points and provide quantifiable, high-value solutions. Jay has more than 15 years of experience in software development, product management, and marketing, and numerous successful product launches. Jay graduated summa cum laude and received a Bachelor of Science in Computer Science from the University of Louisiana at Lafayette, a Master of Science in Computer Science from the University of Illinois at Urbana-Champaign, and an MBA from Rice University.