How RPA is Transforming Finance and Accounting

MaryBeth Folger News

RPA for finance and accounting

Finance and accounting (F&A) processes, by their very nature, are complex and dependent on accuracy, which can create a burden on the department when relying on human labor. According to the McKinsey Global Institute, Robotic process automation, or RPA, is estimated to have a global potential of 44% in F&A. RPA is an ideal solution for F&A processes that increases productivity, prevents delays when dealing with accounts, and reduces inventory turnover. Automation can reduce or even eliminate the issues caused by human labor, offering organizational benefits like increased accuracy, reduced cycle time, and greater regulatory compliance.

According to an Economist Survey, “Advance of Automation,” 59% of finance and accounting leaders believe that RPA will help increase their competitive edge over the next two years. While the digitization of F&A departments has been underway for some time, COVID-19 has further motivated finance organizations to accelerate digital transformation efforts. As a central component of digital transformation, RPA can transform many common F&A processes into functions that add business value. RPA’s capabilities can also be further extended with the addition of artificial intelligence (AI), including machine learning (ML), optical character recognition (OCR), or natural language processing (NLP) to enable better management of unstructured data.

One global financial services company incorporated RPA as part of its digital transformation to automate financial control tasks and processes. The results were impressive: same-day profit and loss delivery, accelerated period-end closing by five days, reduced late posting by 99%. The improved ledger and reporting accuracy and timeframes for a 40% reduction in operating costs.

Strong use cases for RPA in finance and accounting include:

  • Record-to-Report – Unlike humans, robots are specifically designed to compare or reconcile large data sets, or identify exceptions with speed and accuracy. Processes like intercompany accounting, journal entries, GL reconciliations, period-close, statutory and management reporting, and variance analysis all fall into the category of ideal automation candidates. Robots can be trained to assist with regulatory and compliance activities, or to provide support for audits and taxes as well. Any rules-based decision-making can be replicated in a robot’s instructions to be executed with 100% accuracy every time the function is performed.
  • Order-to-Cash – There are a number of areas where robots can be utilized in O2C, as the nature of the work itself is robotic. For human employees, this type of work is draining and tedious, which leads to inaccurate processing and costly rework. As we learned in the bullet above, robots are great at evaluating, comparing, and reconciling large data sets, making them a logical component in master data management. However, robots can also be trained to process cash applications, run invoices, run reports, and even manage credit risks or disputes.
  • Source-to-Pay – Similar to other F&A functions, robots can be used for reconciliations, payment processing, and running reports. Robots can also be trained to manage purchase orders and invoices. Using the same systems as human employees, robot can input and reconcile data from system to system, manage vendor compliance, or even identify errors for manual review by a human.
  • Financial Planning and Analysis (FP&A) – Thinking in terms of automation, FP&A processes are broken into three categories: transactional, analytical, and specialty. Transactional processes like periodic budgets and financial forecasts, performing consolidations, processing eliminations, and running regulatory reports are all well within a robot’s capabilities. Analytical functions like variance analysis against forecasts and budgets, determining and measuring cost drivers, tracking performance and other functions which require more cognitive inputs can be automated, but the process is less straight-forward. AI capabilities are used in this area to help robots mimic certain components of human decision-making, but this requires significant guidance from human employees to properly train the robots over time. Specialty or strategic functions like asset resource deployment and utilization, optimizing customer and product mix, or continuous cost improvement management are not ideal for automation, at this point.
  • Data consolidation and management – Most companies use multiple applications to manage the organization. Each application serves as a point of entry for new or revised data, which can create headaches for the business when applications lack interoperability. To compound that, human errors lead to rework of processes and data management issues as well. Robots can easily evaluate and consolidate data from multiple systems to enable better data management.