Applications of RPA in Corporate Accounting

RPA has been applied to a variety of use cases in corporate accounting. A Gartner study found that RPA can save 25,000 hours of avoidable rework in accounting departments at $878,000 for an organization with 40 full-time accounting staff (Lavelle, 2019). This study involved more than 150 corporate controllers, chief accounting officers, and chief accounting leaders in determining the main benefits of RPA. However, the same survey found that only 29% of RPA adopters have implemented technology for financial reporting purposes. Thus, there is a significant opportunity for accounting and finance departments to leverage RPA to make financial reporting more efficient, accurate, and less costly. Processes that are well defined, rules-based, high volume, predictable, and repeatable are ideal for automating.

Eline Oh, ACMA, CGMA, CPA (Australia) implemented RPA for cash management at an investment firm by hiring contractors who customized the RPA implementation. After implementation, employees no longer had to fund investment bank accounts manually or to move funds between inter-company accounts (O’Neill, 2020).

Gartner features a Fortune 500 multinational company that hired the consulting firm WonderBotz as a case study (Tornbohm & Leiter, 2019). WonderBotz built a customized reconciliation tool called ReconBotz using Blue Prism’s RPA platform for the general ledger reconciliation process, which was highly manual. By using ReconBotz, it automated 95% of its 5,000 reconciliations per month. Typically, it would take an accountant 30 minutes to complete one reconciliation, for a total of 150,000 minutes or 2,500 hours. After implementing RPA (ReconBotz), this process was reduced to three hours to complete all the reconciliations.

Implementation of RPA

The abundance of RPA software providers combined with the relative ease of building robots to automate simple processes might cause accountants to underestimate what it takes to implement RPA for more involved initiatives effectively. As Gartner states, “once organizations move beyond simple examples, there is a critical need [sic] for multidisciplinary governance and coordination across business units, IT, security, sourcing and assurance functions. Without this comprehensive approach, many organizations experience buyer’s remorse due to poor RO1, misaligned resources, siloed usage, and inability to scale.”

(Stoudt-Hansen et al., 2019). Thus, accountants must consider the proper circumstances under which to implement RPA carefully.

A Financial Management article recommends considering the following three key areas before introducing RPA (O’Neill, 2020): select a process, clearly define the scope, and get buy-in from staff.

Selecting a process refers to management being incredibly careful with the initial project selection. As stated in the O’Neill 2020 article, one school of thought is to experiment with a process to get a “proof of concept” to demonstrate how it works and ensure staff is used to the idea of RPA. The problem with this approach is that they are unlikely to yield a meaningful return on investment. A second approach is to document and measure existing processes using key performance indicators. After evaluation of the results, select the process to automate.

Clearly defining the scope refers to management setting reasonable expectations regarding RPA capabilities. By doing so, staff will be more likely to have a balanced assessment of the results of automation and less likely to focus on the failures of RPA. As Rand Low, a senior fellow at the University of Queensland’s Business School in Australia, states, “Humans are reluctant to trust an automated system and will focus on the rare moments where the RPA fails rather than on when it succeeds” (O’Neill, 2020).

Getting buy-in from staff recognizes that employees may be reluctant to embrace RPA for fear of being replaced by robots and losing their jobs. Reassuring employees entails communicating that RPA will upskill staff and keep them on the forefront of industry developments. Reassurance requires investments in teaching staff how RPA works and training on how to use it.

To shift perceptions, managers and employees need to talk about how responsibilities and processes will change, what training will be available, and if professional development can be pursued in place of manual tasks that will be removed from a team’s workweek.

(O’Neill, 2020, para. 15)

Gartner provides three additional recommendations to consider that are more technical: (1) explore the relative advantages of various RPA products by distinguishing between the needs of the developers and those required for sufficient scale across the organization; (2) optimize the likelihood by defining the corporate use cases and the capabilities essential to the business and aligning them to strategic objectives in the long term; and (3) maximize RO1 by prioritizing and selecting an RPA product that has broad functional capabilities to support the primary core functions of the business, while also paying close attention to tangential capabilities, such as Al and ML (Miers et al., 2019). This type of analysis ideally is performed by a team of accounting, finance, IT, and strategic management personnel working together.

Why RPA Fails

Despite the increasing adoption of RPA across industries, the mistakes that many organizations make during implementation have resulted in a 30% to 50% failure rate on initial projects (EY, 2016). According to EY (2016), the top 10 common issues for failed RPA projects are:

  • 1 Not considering RPA as business-led, as opposed to IT-led
  • 2 Not having an RPA business case and postponing planning until after proofs-of-concept (POCs) or pilots
  • 3 Underestimating what happens after processes have been automated
  • 4 Treating Robotics as a series of automation versus an end-to-end change program
  • 5 Targeting RPA at the wrong processes
  • 6 Applying traditional delivery methodologies
  • 7 Automating too much of a process or not optimizing for RPA
  • 8 Forgetting about IT infrastructure
  • 9 Assuming RPA alone is sufficient to achieve an excellent RO1
  • 10 Assuming the skills needed to create a POC are good enough for production automation, (pp. 4—10)

The most complicated issue to deal with when it comes to RPA failures relates to the people, not the technology. The first consideration noted by EY speaks to the importance of management involved with an automation project. Deploying robots will result in a virtual workforce across the entire organization: Consequently, business leaders, not IT specialists, should be responsible for prioritizing which processes are automated for managing the workforce. The business leaders should collaborate with IT, Cyber, Security, Risk Management, and others.

Another implementation challenge is pursuing RPA development with in-house teams that do not have sufficient training or capacity (Al Multiple, 2020).

Integrating RPA with AI/ML Applications

Van der Aalst et al. (2018) suggests that RPA must become smarter through integration with Al and ML techniques for adoption to become more pervasive. The more RPA can be integrated with Al and ML applications, the more significant the potential impact on the accounting profession. Gartner ranks the most common ways in which AI/ML is implemented in RPA products, categorized by use: (1) RPA paired with computer vision algorithms, (2) optical character recognition to read and interpret the text; (3) automated business process and task discovery, (4) augmented content analytics, and (5) NLP and natural language generation (NLG) (Miers et al., 2019). All these integrated implementations could provide substantial benefits to the accounting profession going forward.


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