How to Achieve Success with RPA in Finance Automation

by Jesse Spencer | March 30, 2020

With all the options for finance and accounting data automation, navigating the technological landscape is tricky. If you’re looking for a software solution to automate manual processes, robotic process automation (RPA) will certainly be on your radar.

RPA solves some problems, but it isn’t an automation silver bullet.

One thing is for sure though – manual processes are highly inefficient, error-prone, and expensive. While solving the reason for so much manual effort seems like a logical thing to do, it is often just not realistic. The use of multiple accounting systems is often the result of mergers and acquisitions, and attempting to streamline operations with a single solution might just be too disruptive. Whatever the reason for the fragmentation of back-end finance systems, automation is certainly the answer.

The most important thing to understand about RPA is that it is dumb. There is zero intelligence built into RPA. It cannot read, understand, or interpret data.

And if you have a lot of this rekeying going on, prioritize the most error-prone tasks first. But RPA isn’t just about accuracy. Data quality and maintaining compliance are hallmarks of successful RPA implementations.

The Cause of RPA Failure

One of the number one causes of failure for RPA projects is a misunderstanding of its ability to scale within the organization. Because RPA is not an intelligent solution, the use-cases are extremely narrow. While it’s perfect for the scenario of rekeying already-digital data, it cannot be used in other processes which deal with with unstructured or analog data.

Another factor in the declining success of RPA is the assumption that its value is in labor reduction alone. While RPA can automate certain manual tasks, the bigger savings really comes from better quality business processes.

If you can’t tie a financial win to achieving better business outcomes, RPA is not going to be a silver bullet.

Solving the Deeper Need in Finance and Accounting Systems

The biggest challenge in finance and accounting automation is getting content from other business documents like invoices or freight bills into software applications. Combining RPA with an optical character recognition (OCR) and machine learning (ML) tool is a highly effective strategy to ensure success.

Intelligent document processing tools combine OCR, ML, and data sciences tools like computer vision to extract relevant information from unstructured and analog data sources. They are human-in-the-loop solutions that empower subject matter experts to work with difficult data.

Because successful finance automation is all about improving business outcomes from better processes, all RPA implementations should include a solution for intelligent document processing.



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