December 28, 2022

Measuring Automation Success: Does Straight Through Processing Matter?

Learn about the importance of straight through processing and it can help in measuring automation success.

With regards to document processing, the goal of STP is to process documents with no human intervention. Once documents have been processed by a traditional OCR solution, error correction is handled by dedicated employees. Businesses frequently use STP to automate back-office functions. The goal of using STP is to reduce document processing times, minimize errors, and reduce expenses.

With STP, it’s possible to remove humans from the equation entirely, therefore reaching automation rates of 100%. This can be misleading, however, as automation rate doesn’t account for time spent handling exceptions—more on this later.

Straight Through Processing

Naturally, the idea that you can be completely “hands off” with document processing is highly appealing. It makes sense to assume that pushing documents through a workflow without manually touching them will save time, and therefore, money. Because many organizations process thousands of documents per day, manually processing every document can cause massive slowdowns. To combat process bottlenecks, businesses look to STP to keep document-related data accurate, secure, and easily accessible for decision making. Unfortunately, when it comes to traditional automation initiatives like OCR, measuring straight through processing fails to show how the technology influences business performance. The ability to complete tasks successfully without the need for manual intervention is often referred to as straight through processing. Tasks can move “straight though” in a fully-automated manner. This capability relies upon a very important factor: the ability to determine with high precision that a task was not just executed, but that it was executed correctly.

Automating Simple Tasks

For simple tasks, we accomplish this through task validation. For example, rather than create a script to provision an email account and presume that everything was done correctly, validation routines can be created to send an email and verify that it was sent and received. We can verify that the email address created matches the established naming convention and that it is for the intended employee. All of these checks are done to identify correctness of a task.

Automating Task Verification

These checks are critical because they verify the actions of an automated task. After all, we certainly don’t want to blindly rely upon our Robotic Process Automation (RPA) to provision Information Technology resources when things are actually done incorrectly. Without automated task verification, the alternative would be to manually verify the output of each task. Just as you would not expect to “approve” every single action of an autonomous car, organizations clearly do not expect to manually check every single result. Without a high level of STP, true automation is never achieved.

Achieving STP for Complex Tasks

What about more complex tasks that cannot be completely automated (like those discussed in our Part 1 article)? Unlike simple tasks that can be easily automated and verified, the process of locating and extracting 20-30 data elements on a document rarely can be summarized as a simple pass/fail. Even if all of the data on a single document could be located and extracted, what type of validation can be accomplished to determine if it is all correct?

The process of achieving STP on complex document-oriented processes is not as straightforward as simpler tasks. Unfortunately, this complexity has resulted in a significant number of organizations using advanced capture solutions to automate tasks only to find that they need to verify every single result. The upside is that while the answers to achieving STP are not as straightforward as what you can expect with simpler tasks, it is definitely possible. It just requires a different approach: one based upon data science.

An Example of failed STP 

Imagine working with a public sector organization, where you found that their legacy OCR tool resulted in a 30% exception rate for a specific document type. To correct these exceptions, assume that it takes an average of 15 minutes per document. Extrapolating this time over the year will thus equate to a cost of over $100k—just to fix the errors in this one document type. However, once this organization realizes  how much revenue is being lost due to the lengthy exception handling process, they can decide to implement an IDP solution for its ease-of-use and ability to accurately extract data from complex documents.

With the new ML-led solution in place, processing the same document type with a human-in-the-loop ensured the highest level of accuracy during the extraction, decreasing the costs by over 80% for the year.

Measuring What Matters: Average Handling Time

One of the primary goals of any document processing solution is to help businesses save time and effort. To calculate time saved, you wouldn’t look at straight through processing rates—you’d look at how long documents took to process before a solution was implemented, and how much time they take to process after a new solution is introduced. This is the concept of average handling time.

Average handling time is so useful because it takes into account all the limitations of the technology you use. When you’re measuring how long it takes to accomplish something, it doesn’t matter how much of the process is automated—if the total time spent on the entire process isn’t any faster, an STP rate of 100% offers no tangible value toward the goal of saving time.

Furthermore, if you wish to compare document processing solutions, pitting a traditional OCR solution that offers “100% straight through processing” against an intelligent document processing solution (IDP) that uses a human-in-the-loop process to prioritize accuracy (and therefore time savings later), using straight through processing to compare the two won’t result in a fair comparison.

Instead, the best metric to compare the two is average handling time—and the amount of effort needed to produce accurate results that benefit customers.

Prioritize Business Impact Over Technology Performance

Implementing an intelligent automation solution is a step taken to help achieve business goals, not to reach automation milestones. And while OCR can help with this, remember that 100% straight through processing rate isn’t the way to get there. Instead, increases in efficiency are better measured by time saved. Additionally, solutions that take different approaches must be compared on an equal footing. When it comes to evaluating success of document processing solutions, that footing is average document handling time.

To explore how an intelligent document processing solution can help your organization automate more effectively, watch our Platform demo where you’ll see how to classify and extract data across complex, difficult-to-read documents, including handwritten forms, PDFs, images, emails and more.

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