Robotic Process Automation (RPA) is transforming finance departments worldwide. By automating high-volume, repetitive tasks, RPA enables finance teams to reduce costs, boost efficiency, and focus on more strategic initiatives.
As a data analytics leader with over 10 years of experience in process automation, I‘ve seen firsthand the game-changing benefits of RPA in finance. In this comprehensive guide, we‘ll explore the top 9 use cases and examples, including detailed analyses and the latest market research.
The Rapid Rise of RPA in Finance
Finance dealt with enormous data volumes and transactional work long before RPA emerged. But early adoption of RPA in functions like IT and customer service paved the way for finance to benefit.
The finance use case is compelling – as McKinsey notes, 42% of finance activities have automation potential. With relentless pressure to optimize costs, it‘s no surprise that RPA adoption has soared.
According to Deloitte’s 2021 RPA survey, 72% of CFOs have implemented RPA in finance to some degree already. The most adopted use cases include:
Deloitte: RPA Use Cases Adopted in Finance
As the chart shows, leaders see RPA as a tool to both reduce costs and enable growth. The pandemic accelerated adoption, with finance needing to handle huge volumes of relief programs and payments efficiently.
Let‘s examine the top 9 use cases yielding the biggest benefits.
1. Accounts Payable and Receivable
Processing supplier invoices, customer payments, and cash applications comprises over one-third of finance headcount by some estimates. With high volumes and repetitive data entry, AP and AR are prime areas for automation.
Accounts Payable
Typical AP activities automated by RPA include:
- Retrieving emails and scan files with supplier invoices
- Extracting invoice data and cross-checking PO matches
- Routing invoices for approvals
- Updating accounting systems
- Reconciling accounts and managing exceptions
- Processing electronic payments
- Managing supplier master data
RPA drastically reduces the manual effort of getting invoices into the system, routed, approved, paid and recorded correctly. With up to 70% savings on AP processing costs, it’s the most common finance use case for a reason.
Accounts Receivable
On the AR side, automation handles:
- Generating and sending customer invoices
- Importing and posting incoming payments
- Matching receipts to invoices and orders
- Credit checking new customers
- Managing disputes and deductions
- Following up on delinquent customers
Top teams use RPA to process 85% more receipts with the same headcount according to JPMorgan. With faster cash application and reduced DSO, it directly improves working capital.
2. Financial Reporting
As a public company CFO, nothing kept me up at night like filing accurate financial statements on time. RPA helps automate this intensive, deadline-driven process from data gathering through narrative generation.
Typical reporting activities performed by RPA bots include:
- Retrieving data from ERP, CRM, and other systems
- Comparing current periods to prior results
- Running validation checks based on thresholds
- Populating reporting templates with tables and charts
- Adding explanatory narratives and notes
- Facilitating review and sign off workflows
RPA ensures a complete audit trail and version control for each report. It also eliminates dependencies on specific team members.
According to AICPA research, over 50% of finance professionals reported improved reporting speed and accuracy with RPA adoption.
3. General Ledger Reconciliations
Reconciling high volumes of sub-ledgers, bank statements, and control accounts is a manual, repetitive chore. RPA helps accelerate matching transactions and resolving exceptions.
Typical reconciliation activities performed by bots include:
- Downloading data from 100+ systems daily
- Matching sub-ledger vs. GL account entries
- Identifying exceptions based on variance thresholds
- Creating reconciliation reports for reviewers
- Posting correcting journal entries
- Maintaining thorough documentation
Automating data aggregation and matching frees staff to research and address root causes of discrepancies.
According to an EY study, RPA reduced reconciliation times by 60-80% across multiple clients. That translates to massive savings.
4. Audit Support
As someone who‘s managed audits from all sides – internal, external, IT, financial – I can personally attest to their labor intensity. RPA helps audit teams scale efforts by automating tedious but necessary tasks.
Common use cases include:
- Selecting random samples from source systems
- Extracting support (invoices, contracts, etc.)
- Testing controls and validating compliance
- Importing data from auditees
- Analyzing trends for anomalies
- Compiling results into audit reports
With RPA gathering samples, validating testing, and documenting steps, auditors can focus expertise on higher judgement areas and exceptions.
Per Deloitte, over half their audits now leverage RPA – a number growing exponentially each year. The benefits are too big to ignore.
5. Cash Applications
Incoming customer payments often lack the required remittance details to properly apply receipts. Cash apps teams must manually research and determine the right accounts. RPA accelerates this process by intelligently matching payments to invoices and orders.
Typical activities automated by RPA include:
- Retrieving electronic payments and remittance files
- Identifying intact vs. exception payments
- Posting standard payments by matching amounts
- Flagging exceptions – partials, overpayments etc.
- Applying unidentified payments via algorithms
- Minimizing deductions through better matching
These capabilities help leading teams process 85% more receipts per FTE. Staff productivity soars while DSO drops.
6. Payroll Processing
Few activities have as little tolerance for error as payroll. Automation enables finance to handle high volumes of employee time data while maintaining compliance.
Common RPA payroll use cases include:
- Importing hours/wage data from HR systems
- Calculating gross and net pay based on policies
- Determining withholding amounts following latest tax rules
- Generating and distributing pay slips
- Integrating payroll data into GL and HR systems
- Managing regulatory reporting and filings
By automating computations and compliance checks, RPA reduces manual work required to pay employees accurately and on time.
Per KPMG, RPA adoption for payroll processes tripled from 21% to 57% from 2017-2019.
7. Regulatory Compliance
Between SOX, GDPR, and other regulations, finance must manage and demonstrate compliance with volumes of controls and data retention policies. RPA helps automate:
- Performing control testing using random sampling
- Monitoring systems for access violations
- Archiving emails/documents for e-discovery
- Anonymizing PII data fields pre-migration
- Generating audit reports for regulators
This saves massive manual effort while reducing the risk of non-compliance. According to Protiviti, nearly 50% of finance leaders cite improved compliance as a driver of RPA adoption.
Regulation will only intensify – RPA is key to remaining compliant efficiently.
8. Financial Planning & Analysis
As FP&A teams evaluate performance and model future scenarios, RPA helps efficiently consolidate data from across the enterprise.
Typical FP&A activities performed by bots include:
- Integrating financial data from all departments and systems
- Generating audit trails for analysis performed
- Loading actual results and comparing to budgets
- Creating models and projections based on trends
- Distributing insightful reports to leadership
According to Protiviti, over 50% of FP&A teams are expanding RPA to provide the visibility needed for agile planning.
9. Data Migration
In the past 5 years, I‘ve led two major ERP replacement initiatives. Both required migrating massive volumes of legacy data to new systems. RPA was instrumental in these efforts by automating the ETL processes.
Typical RPA data migration activities include:
- Extracting data from databases, files or outdated applications
- Standardizing, cleaning, and mapping data to new structures
- Moving data securely into new platforms
- Validating successful migration
Per HyperScience, RPA improves data migration speed over 75% while achieving 99%+ accuracy. That level of precision is impossible manually.
Evaluating RPA for Your Finance Needs
The use cases and benefits highlighted here represent just a subset of automation opportunities with RPA. Almost any repetitive finance process is a candidate.
When evaluating RPA, focus first on high-volume, repetitive tasks with defined rules and structured data. Processes involving multiple legacy systems with manual handoffs are prime targets.
Start with a contained pilot like AP or general ledger reconciliations. Measure results, capture lessons learned, and expand from there.
With the right approach, RPA can transform finance operations to be leaner, faster, and more strategic than ever before. The future of finance lies in human + machine collaboration. RPA is the critical technology making that future possible.