RPA in finance and accounting: let the robots do the math

RPA in finance and accounting: let the robots do the math

April 28, 2021

Daniil Gavrilchik

RPA Business Analyst

The bad news is that robots can't pay taxes for you. After all, they are by definition heartless and therefore little prone to showing altruism. What about the good news? Well, they can at least streamline and speed up fiscal procedures, as well as helping manage many other financial and accounting tasks.

Obviously, we are not talking about physically existing robots, but about software bots, the real backbone of robotic process automation (RPA).

Taking finance to new levels

As much as we try to push finance towards models with a strong strategic, analytical and decision-making component, we still have to combine these activities with an immense amount of daily paperwork and tedious, dehumanizing tasks.

Among these, we may count manual filling of numerous forms, documents, and balance sheets, recording of transactions, and many others. Fortunately, most of these processes are repetitive and rule-based. Thanks to these features, they have proved to be ideal subjects to be managed, partially or completely, by software robots.

Bots are generally adopted to assist humans in performing high-volume standardized tasks with greater speed and accuracy. They don't get tired and don't need a coffee break: they can work 24/7 with a close-to-zero error rate.

This made the adoption of robotic process automation solutions in finance and accounting departments particularly successful and widespread, granting faster data processing times and better compliance with the typically strict financial and tax regulations.

A couple of figures on the table

The success of RPA in finance is proved by numbers. According to Grand View Research’s 2021 RPA market analysis, the BFSI segment (a macro-group including banking, insurance, and financial services) is the undisputed leader in RPA adoption, alone accounting for 29% of RPA's global market revenues.

Based on Protiviti's 2019 Global RPA Survey, financial services are quite ahead also in terms of RPA's maturity stage, sharing the top position with the tech/media/telecom industries and boasting a mature or advanced implementation in 29% of the companies surveyed.

This is reflected in the excellent performance boost RPA provides to the financial sector. The respondents indicated moderate, large or very large increases in 46% of cases. The percentage rises to 88% if we consider future expectations for the next two years. Only the IT and telecommunications sector appears to be performing better.

Considering the above, it comes as no surprise that the biggest benefit from RPA in finance cited by 19% of the respondents was actually increased productivity, followed by a stronger market position (18%), higher customer satisfaction (12%), and greater employee gratification due to bots performing trivial tasks (11%).

Such a high maturity phase and significant achievements come at a cost: the financial sector is the one that, right after IT and telecom, invests the most resources in RPA in relation to revenue. In fact, it scores second with 0.12%.

RPA spend as a share of revenue

How to use this powerful tool

Finance and accounting, like any other industry, can benefit from a wide range of RPA use cases. Let's make a brief overview of some of the most important ways to leverage this technology.

1. P&L reporting

P&L reports are a critical component in overall data analysis and reporting processes for any financial services company, as they summarize the revenues and expenses incurred over a given period. These financial statements are compiled by manually combining several individual reports. During this procedure, inaccuracies and errors add up, creating significant headaches for the accounting departments.

For this reason, it can be useful to delegate such tasks to bots that automatically compile, validate, and merge reports. According to our partner UiPath, implementing bots resulted in a 70% reduction in AHT and a 100% accuracy achieved for one global financial services company.

2. Investment and asset management

Software robots can also lend a hand in managing investments.

In a typical asset management firm, analysts periodically receive updated estimates from brokers via email. This data is manually merged, entered into master totals spreadsheets, and used to create reports and graphs. The whole procedure can be automated via bots, which open the emails autonomously, extract the relevant data, and process it to create reports with useful insights into the stock market’s trend.

3. Reconciliation

Bank reconciliation is a delicate operation that involves verifying the correspondence between the actual balance of a bank account and that reported in the most recent bank statement. The anomalies are quite frequent and can be due to, for example, human errors of compilation, the failure to present a check already issued to the bank, or delays in recording transactions in the institution's books. This process may seem even more complicated when you consider that each financial organization can rely on different systems and follow distinct standards.

Robots can manage the cross-checking of cash flow, searching for any inconsistencies between payment details and bank records, and triggering a request for further record validation if something is wrong. If, on the other hand, there are no discrepancies, the bots will compile and send a report to the administration for approval.

4. Accounts Payable

Handling huge amounts of incoming invoices from different suppliers and through multiple channels (such as fax and email) is the nightmare of every accounting department. In fact, accounts payable activities are among the most tedious and error-prone, but they still need to be done quickly and accurately to avoid conflicts with suppliers and consequent penalties.

In this regard, bots prove to be a precious ally, thanks to their ability to extract data from invoices of different formats and re-insert them autonomously in standardized and easily accessible forms. Furthermore, they can dispatch these reworked invoices to the right accountants and, if necessary, send a reminder in case of late payments.

5. Accounts Receivables

If it's clear that payables to suppliers should be honored on time, imagine the rush that companies go through to collect their receivables. Once again, bots come in handy for streamlining and speeding up account receivable activities.

Among the tasks that can benefit from RPA, we can mention the validation of purchase data, the processing of invoices, emailing to customers, and creating reports. In this way, the time required to collect payments (namely DSO) is significantly reduced, avoiding annoying cash gaps.

6. Tax reporting

A final noteworthy use case of RPA in the financial and accounting field concerns tax reporting, which should be fulfilled with the utmost precision for obvious reasons of legislative compliance. Extracting and combining data from multiple sources to compile the necessary tax documentation is child's play for bots.

Major financial institutions have understood the potential of RPA, including Ernst & Young. Their Shanghai branch has developed a similar solution for one of its clients to automate VAT filing tasks and reduce processing time from 1,400 to 280 hours.

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Top challenges and possible solutions

Despite the promising statistics and use cases cited above, there is still a lot of work to be done to properly leverage RPA in finance and accounting. The first potential challenge concerns the very nature of this innovation.

According to McKinsey, it would be possible to automate 27% of financial procedures with already available automation technologies. One-third of these tasks may be performed by deploying rule-based bots, while the rest would require AI technologies, including machine learning (ML) and deep learning (DL).

Potential for automation in finance

Delving into the broad range of financial operations, McKinsey's analysis shows how operations related to accounting operations, revenue management, cash dispensing, taxes, and reporting are particularly suitable for automation via today's systems.

What these operations have in common is that they are generally repetitive, high-volume, rule-based, and with few exceptions. Also, they involve standard input or output formats and detailed documentation from which to extract and collect data quite easily.

On the other hand, any financial activity that requires a deeper level of analysis, critical thinking, and data-driven decision making (such as business development or auditing) is unlikely to be fully automated. However, it could benefit from the synergy of RPA tools integrated with business process management systems.

Merging RPA with AI

Implementing AI and predictive analytics in finance appears to be the best way to unlock the full potential of robotic process automation in finance, augmenting bots with cognitive and analytical skills.

Such teamwork may be extremely valuable when it comes to processing data and inserting it into journal entries and other types of financial or fiscal reports. Robots can extract data and compile these documents much faster than humans, but they may have a hard time interpreting any deviations from the patterns they are used to.

Here machine learning comes into play, taking care of identifying and correcting possible inconsistencies between financial records. ML-based systems can also leverage their analytical and forecasting capabilities to get useful insights from the same data that bots would just copy and paste. This information can be invaluable for long-term financial decision-making and handling ever-changing business cycles.

Another possibility offered by artificial intelligence concerns speech recognition and natural language processing, typically based on deep learning technologies. These features, implemented by several RPA platforms such as UiPath and Automation Anywhere, provide bots with even more effective accounting data extraction, which will no longer be limited to standard and extremely simple input formats such as digits. Besides, they unlock the ability to generate comments on reports.

Facing the human factor

So far we have talked about technologies but, despite the name, robotic process automation is something in which humans can count as much as machines. The first purely human factor to consider is the common hesitation to give up human judgment.

According to Gartner's 2019 study on the adoption of RPA in finance, the fear of forgoing human intervention by delegating more responsibilities to robots would be one of the main obstacles to implementing RPA.

Rather, it would be preferable to introduce a short-term tandem system into specific financial activities to show corporate leaders the benefits of full automation over hybrid systems, in terms of performance and accuracy.

Anyway, leaders aren't the only people potentially concerned about implementing RPA in finance and accounting. Like any innovation applied to the labor market, automation can lead to heavy and painful job displacement. The aforementioned Protiviti's survey clearly shows the extent of employees’ concerns but also sheds light on a possible solution:

RPA impact on employees

While it is true, according to 61% of the sample interviewed, that employees fear losing their jobs due to RPA, it is also true that many companies are preparing to handle these issues, grasping the importance of analytical, strategic and interpersonal skills (76%) and planning massive retraining and redeployment of their workforce (64%).

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The best is yet to come

Great hopes are rising around the increasing implementation of RPA in finance. As reported by the Institute of Management Accountants (IMA) in their 2020 statement Transforming the Finance Function with RPA, 75% of the nearly 1,500 attendees at one of their webinars said financial and accounting activities may benefit from RPA moderately to significantly. Furthermore, 34% of them believe that of all emerging technologies, RPA will have the most impact within financial and accounting departments over the following three years.

Such high expectations seem reasonable, considering that, according to Gartner estimates, the increased accuracy guaranteed by RPA and the consequent lower incidence of human error could save an average of 25,000 hours of rework and $878,000 per year.

The key to pushing these positive trends towards new important milestones lies in understanding the limitations of RPA, integrating it with the latest emerging technologies, and fostering the human element with proper workforce retraining.