RPA can automate repetitive finance and accounting work, but strong use cases require clean data, process redesign, governance, controls and human review.
Robotic process automation (RPA) can help finance and accounting teams automate repetitive, high-volume work that depends on structured data, defined rules and predictable system steps.
RPA is especially useful when finance teams still rely on manual work to move data between enterprise systems, spreadsheets, reporting tools, banking portals, procurement applications and ERP finance modules. Bots can copy data, validate fields, trigger workflows, flag exceptions and reduce some of the manual effort involved in closing the books, processing invoices or reconciling accounts.
For years, organizations have tried to improve finance operations through enterprise systems, reporting tools and stopgap processes that reduced some repetitive work but did not eliminate all manual handoffs.
"Unfortunately, what has been discovered is that while these solve many of the existing problems, they haven't solved two of the core problems that exist in the finance and accounting world," said Jeremy Dean, who was a national RPA lead at Mazars USA when originally interviewed.
The first challenge was how to get data into these systems and the second was how to close financials at month's end, Dean said.
"Many are now finding that RPA provides the means for organizations to finally address and solve these problems," he said.
How RPA works
RPA uses software robots, or bots, to emulate actions that people would otherwise perform in business applications. A bot might read information from one screen, copy data, paste it into another application, open a file, send an email or follow a defined set of rules.
RPA does not need to automate an entire finance process to be useful. Instead, it can automate specific steps within a larger workflow, especially when those steps are repetitive, rules-based and time-consuming.
"The quickest wins have been more rules-based processes that are more amenable to RPA," said Dennis Gannon, a VP Analyst at Gartner.
RPA can help organizations improve efficiency, compliance, speed, cost savings and employee productivity by automating repetitive tasks.
In this context, RPA could be thought of as digital duct tape.
"A good candidate [for RPA] is a task that is a bottleneck in a larger process that may take hours of manual work," Gannon said.
Order-to-cash and procure-to-pay processes are common places to start because they include high-volume transactions, structured data and repeatable steps. Starting with those processes allows finance teams to focus on achievable RPA wins, get feedback on what works well and then find more tasks that are easy to automate.
As to fears that the robots are coming for the finance teams' jobs, it's important to include those teams on RPA projects both to allay fears and to find new opportunities, Gannon said. Project leaders can start by inviting a few people from a finance team into an automation lab for a few days a month to practice putting new bots into a production environment. Over the course of the rest of the month they will notice how the bot worked and can identify any in-use problems or limitations. These deep dives can also teach them how to spot other automation opportunities between sprints in their daily work.
"It's not hard to see the opportunities everywhere once you get engaged," Gannon said.
RPA consists of software robots, or bots, that represent reusable automations for tasks and processes. Bots mimic some functions humans typically do, such as reading a screen in one application, copying the appropriate text and pasting it into another application. IT teams can use RPA platforms to create, monitor, manage, reuse and secure bots and their activities.
RPA can be combined with other technologies. Optical character recognition (OCR) can capture text from paper or scanned documents. Machine learning can help map fields in invoices, purchase orders or bank statements. Natural language processing can help extract key terms from contracts or other documents. When RPA is combined with these tools, organizations often describe the result as intelligent automation or intelligent process automation.
AI agents add another layer to that automation picture. Unlike traditional RPA bots, which follow predefined steps, AI agents can use large language models and other AI capabilities to interpret context, choose tools and adapt their next steps toward a goal. In finance, agents might help review exceptions, summarize supporting information or orchestrate work across systems, while RPA bots remain useful for precise, repeatable execution, such as moving approved data into an ERP system.
This kind of automation creates the most value when finance teams treat it as a controlled automation layer, not a shortcut around process design.
RPA can also complement low-code tools, API-based integrations and integration platform as a service. APIs and direct integrations are usually better for stable system-to-system connections. RPA is often useful when APIs are unavailable, legacy systems are involved or a process still depends on application screens that humans use.
This kind of automation creates the most value when finance teams treat it as a controlled automation layer, not a shortcut around process design. Bots still need governance, monitoring, access controls, exception handling and process ownership.
To understand where RPA can help, here are nine finance and accounting use cases.
1. Automating governance
Scaling an RPA implementation can be difficult. One challenge is enabling finance departments to easily create new bots while also providing guardrails.
Governance should define who can create bots, who approves them, which systems they can access, how bot credentials are managed, how errors are reported and how automation performance is monitored. Finance, IT, audit and compliance teams should all understand which processes are automated and what happens when a bot fails.
In the original reporting for this article, Sandeep Singh, who was finance global quality and RPA CoE lead at HPE when interviewed, said Hewlett Packard Enterprise centralized its bot infrastructure to make finance automation easier to scale while maintaining governance and control. His team built out a bot platform on top of WorkFusion RPA. The bot platform helps simplify bot deployment and allows bot modeling, use tracking and error reporting. The team also created an internal governance framework to provide a complete view for stakeholders across audit, business compliance, IT and finance teams.
"This gives not just senior executives, but also the external auditors a sense of comfort that we have our eyes on the ball," Singh said. "We constantly work toward ensuring that nothing slips through the gaps."
2. Reconciling accounts
Comparing account balances between systems is a critical -- but often tedious -- function.
RPA can help with reconciliations by gathering data from multiple systems, comparing balances, checking defined rules and routing exceptions for review. This can be useful when different business units use different templates, approval flows or ERP processes.
One of the biggest gains for HPE came from using RPA to improve journal entry and financial account reconciliations, Singh said. These processes are compliance-bound, time-consuming and involve disparate processes across the organization. For example, suborganizations within HPE have different templates, processes and approval flows. Some processes might involve functions within an ERP system. Some might involve audit and compliance requirements of identifiability for transactions, along with all the respective business requirements on approval flows and amount thresholds.
Singh's team had to undertake a thoughtful process redesign to strike the right balance between the varied nature of business-specific requirements as well as stringent audit and compliance rules to create a streamlined process that his team could automate with RPA.
"While business requirements can be negotiable and are subject to improvisation, accounting rules and compliance requirements have to be dealt with kid gloves," Singh said.
3. Processing invoices
Invoice processing is another promising RPA use case.
In the HPE example, the accounts payable team processed a considerable volume of paper invoices each month and was responsible for recording vendor invoices for subsequent payment processing. Issues came in the form of multiple invoice formats, a range in the quality of scanned image invoices and the use of multiple languages. The company used a combination of OCR and machine learning modules from WorkFusion to mitigate image format and quality challenges.
OCR helps digitize the invoice images into a consistent text format. Machine learning then uses historical rules to determine how invoice fields map to finance applications. HPE used RPA to post the extracted data into SAP Ariba for procurement management.
Finance leaders should avoid automating invoice processing without controls. The process should still account for approvals, duplicate invoices, vendor validation, tax rules, payment terms and exception handling.
4. Purchase order processing
Purchase orders often come in a variety of formats that vary widely depending on the source.
"RPA can automate and speed this process up, as well as reduce human errors," Dean said.
As with invoice processing, OCR can help read paper documents, and machine learning can help map data from the documents into the system of record. For example, Dean worked on a project for a brewer that wanted to automate purchase order creation within its SAP implementation. An RPA bot received input in two different formats, validated the completeness of the data input, then set up the PO shopping cart in SAP and submitted a request for its approval.
RPA can help here, but finance and procurement teams should first standardize the process as much as possible. Automating inconsistent purchase order processes can make errors move faster rather than reduce them.
5. Remediating discrepancies
Once bad data enters the enterprise data ecosystem, it can quickly spread to multiple systems and data repositories. This can result in significant downstream data cleaning and correction work.
Dean worked on several projects that used RPA to scan data, identify issues and bring discrepancies to a team member's attention for review. Finally, once the correct data is identified, a bot can programmatically correct the data issue across all impacted systems.
For example, Dean worked on one project with a logistics company that used RPA to identify discrepancies between the ERP system and the company's reporting tool. The bot evaluated the discrepancy and used various rules to determine if the issue came from an error with the source data or the reporting repository. The resulting decision was flagged for review and approval by a team member. Once the team member approved the change, the bot made the correction in the appropriate system. This increased the data quality across all systems involved.
This kind of use case works best when data ownership is clear. Finance, IT and business teams should define which system is the source of truth and who can approve corrections.
6. Processing cash data
Cash application is a critical function in the accounts receivable process. Applying the right payments to the right accounts and invoices is a process that includes multiple ways to introduce errors.
The HPE cash application team processed a large volume of payments from customers in more than 50 countries, Singh said. This process often started with bank statements that had to be rendered in the appropriate format and copied into the accounts receivable application for a given department or group. RPA automated the process of reading the bank statements and copying data to the appropriate fields in the accounts receivable application.
HPE faced challenges that included varying bank statement formats, multiple languages and missing information that compounded the work of accounts receivable analysts, Singh noted. In response, his team developed an RPA workflow that used fuzzy logic to improve data identification and machine learning to avoid repeating previous posting errors. The automation helped improve cash application accuracy and reduce processing time.
7. Ensuring vendor contract compliance
Ensuring that suppliers adhere to agreed-upon contract terms is another promising RPA use case for finance and procurement teams.
In another HPE example, the company's contract compliance team used RPA to help automate processes involved in checking vendor contract compliance.
"The activity is highly manual, cumbersome and fatiguing," Singh said.
In that example, RPA bots scanned contracts and purchase orders and used natural language processing to extract key information such as discounts, rebates and penalty clauses. The bots then compared that information with HPE's ERP data to identify gaps and highlight discrepancies.
Finance, procurement and legal teams should still define the review and approval process. Contract language can be nuanced, so RPA should usually flag issues for human review rather than make final contract-interpretation decisions.
Singh said the use of RPA significantly reduced the manual effort previously involved in the process.
8. Reporting P&L
Profit and loss (P&L) reporting is another use case for RPA.
Many enterprises use RPA to automate P&L reporting, particularly in companies that need to provide daily reports to management, said Manish Chawla, an associate director in Protiviti's Technology Consulting practice.
RPA can reduce the amount of manual, repetitive and time-consuming work performed by finance experts so they can focus on more valuable activities, such as P&L reporting, Chawla said. He noted many firms had cut processing time significantly and provided earlier access to reports with higher accuracy.
RPA is most useful in P&L reporting when the data sources, calculations and reporting formats are stable. If finance leaders need more flexible analysis, dashboarding or predictive insights, they may also need reporting, analytics or planning tools beyond RPA.
9. Managing data across multiple systems
Finance teams often need to manage data residing in multiple systems. RPA can help improve data management across these systems, while also enforcing business rules associated with the movement of this data.
IT teams can build RPA finance automation to trigger on events in these systems, or bots can run at a specific time when a process must be completed, Dean said.
Dean implemented one system for a banking and insurance company that wanted to improve various processes involved in master data management and financial account maintenance. For example, they used RPA to automate three back-office processes related to the seizure of financial assets for customers based on official legal requests made by executors. This made it easier to kick off one process that could access various databases and freeze relevant amounts across various accounts and two different core banking systems with minimal team member interaction. Employees could then focus on more judgment-oriented tasks, such as reviewing and validating the data being updated.
This type of use case requires strong controls. Finance and IT should confirm who owns the data, which systems are authoritative, which changes require human approval and how the organization will audit automated updates. Finance-IT collaboration is especially important because RPA projects affect systems, controls, data ownership and process design.
RPA in finance and accounting works best when teams choose the right processes, not just the most visible pain points. Strong use cases tend to involve high volume, clear rules, stable systems, structured data and measurable savings or risk reduction. Finance, IT, audit and compliance teams should also decide when RPA is the right tool and when a direct integration, ERP workflow, low-code application, AI-enabled process or AI agent may be a better fit.
Editor's note: This article was originally published in 2020 and was updated to reflect current RPA, finance automation, AI, governance and integration considerations.
George Lawton is a journalist based in London. Over the last 30 years, he has written more than 3,000 stories about computers, communications, knowledge management, business, health and other areas that interest him.