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Robotic Process Automation (RPA) – Trends and application in Finance

 
 

Robotic Process Automation (RPA) Overview

Robotic Process Automation, or RPA, involves using software bots to mimic human keystrokes and mouse movements while interacting with computers, automating business processes or tasks. RPA is ideal for repetitive tasks and has applications across various industries, including finance, accounting, and risk management. Benefits of RPA include cost reduction, increased speed, error reduction, and predictability.

Evolution and Sophistication of RPA

RPA has evolved significantly:

  • Stage 1 – Trigger-Based RPA: Usage of Excel macros.

  • Stage 2 – Screen Mashup RPA: Logging on to multiple systems and performing tasks.

  • Stage 3 – Enterprise RPA: Current standard with more complex automation.

  • Stage 4 – Cognitive and AI-Based RPA: Utilizes machine learning and AI for more sophisticated automation.

RPA sophistication can also be broadly classified as:

  • Robotic Desktop Automation (RDA): User assistance required, software resides on a desktop.

  • Unattended RPA: Fully automated, software resides on central servers or cloud-based systems.

With advancements in tools like RPA, the level of automation in business processes has been increasing. Currently, automation may cover around 40% of tasks, which is expected to rise to 60% in the near future. Pricing for services needs to factor in this trend.

Popular RPA Tools

There are various RPA tools in the market, classified based on execution success and innovation capabilities. Notable RPA vendors include:

  • UiPath

  • Blue Prism

  • Automation Anywhere

  • Nintex RPA

  • Microsoft Power Automate

  • Another Monday

  • Nice

  • Pegasystems

  • Kofax

  • OpenConnect

  • WinAutomation

Adoption of RPA Tools

The process of adopting RPA tools is similar to other software selection and deployment processes:

  1. Identify the Process: Determine the tasks or processes to automate.

  2. Shortlist Tools: Select potential RPA tools.

  3. Select Tool: Choose the best tool for the task.

  4. Map to Process: Integrate the tool with existing processes.

  5. Test: Run tests to ensure effectiveness.

  6. Rollout and Support: Implement and support the tool across the organization.

An agile methodology using ‘sprints’ is useful for RPA implementations. Enterprise-level strategy involves:

  • Initialize Phase: Educate about RPA and create a plan of action.

  • Industrialize Phase: Implement prototypes or small projects with RPA.

  • Institutionalize Phase: Roll out RPA across business units and multiple geographies.

Latest Trends in RPA (2024)

  1. Generative AI Integration: RPA enhanced by AI, making it easier to create bots with guided navigation and chatbot assistance.

  2. Market Consolidation: The RPA market is consolidating, with large software vendors entering the market.

  3. Enhanced Data Management: New data management methods are being integrated into RPA.

  4. Increased Automation Levels: Automation levels are expected to rise from 40% to 60%.

  5. Low-Code/No-Code Development: Enabling non-technical users to create applications.

  6. Hyperautomation: RPA is a key component of hyperautomation, integrating with other automation technologies.

  7. Sustainable Technology: Focus on environmentally friendly solutions.

  8. Edge Computing & 5G: Enhancing real-time data processing and reducing latency.

  9. Digital Supply Chain Transformation: Leveraging digital tools to streamline supply chain operations.

  10. Immersive Workplace Technologies: Using virtual and augmented reality for remote collaboration and training.

Applications of RPA in Finance

Robotic Process Automation (RPA) has significant potential to transform finance functions within organizations. By automating repetitive and rule-based tasks, RPA can enhance efficiency, reduce errors, and allow finance professionals to focus on more strategic activities. Here’s how RPA can be applied in various finance functions:

  1. Accounts Payable (AP) and Accounts Receivable (AR):

    • Invoice Processing: Automating the extraction of data from invoices, validation against purchase orders, and entry into the accounting system.

    • Payment Processing: Automating the scheduling and execution of payments.

    • Collections: Sending reminders for overdue invoices and tracking payments.

  2. General Ledger (GL) and Financial Reporting:

    • Reconciliation: Automating the matching of transactions in the GL with bank statements, credit card statements, and other financial records.

    • Reporting: Generating financial reports and dashboards, ensuring timely and accurate reporting.

  3. Expense Management:

    • Expense Report Processing: Automating the submission, approval, and reimbursement processes for employee expenses.

    • Policy Compliance: Ensuring that expenses comply with company policies by automatically checking entries against predefined rules.

  4. Payroll Processing:

    • Data Entry: Automating the entry of payroll data into payroll systems.

    • Compliance and Reporting: Ensuring compliance with tax laws and generating necessary reports.

  5. Tax Compliance:

    • Tax Data Preparation: Collecting and preparing data for tax filings.

    • Filing and Payments: Automating the submission of tax returns and payment of taxes.

  6. Internal Audits and Controls:

    • Audit Trail: Maintaining a detailed audit trail of all automated processes.

    • Compliance Checks: Performing routine checks to ensure compliance with internal policies and external regulations.

Benefits of RPA in Finance

  • Cost Reduction: Reducing the need for manual labor in repetitive tasks.

  • Increased Efficiency: Speeding up processes and reducing the time needed for task completion.

  • Error Reduction: Minimizing human errors associated with manual data entry and processing.

  • Scalability: Easily scaling up operations without a proportional increase in costs.

  • Improved Compliance: Ensuring adherence to regulatory requirements with consistent and accurate processes.

  • Enhanced Data Accuracy: Providing accurate and timely data for decision-making.

Future Trends in RPA for Finance

  1. Hyperautomation: Integrating RPA with AI, machine learning, and other advanced technologies to create more intelligent automation systems.

  2. Predictive Analytics: Using RPA data to predict financial trends and inform strategic decisions.

  3. Process Mining: Analyzing business processes to identify further automation opportunities.

  4. Cloud-based RPA: Leveraging cloud infrastructure to provide more flexible and scalable RPA solutions.

  5. Increased Adoption of Low-Code/No-Code Platforms: Allowing finance teams to build and customize automation workflows without extensive programming knowledge.

By embracing RPA, finance functions can become more efficient, accurate, and strategic, ultimately supporting better business outcomes

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