Despite unprecedented advances in workplace technology, many accounting and finance professionals continue to struggle with highly manual and inefficient month-end close processes.
Recent advances in robotic process automation (RPA) and machine learning (ML) have delivered a blueprint to transform and modernize the office of finance. By adopting a continuous accounting framework, finance practitioners can increase the quality, accuracy, and efficiency of their financial operations.
Continuous accounting embeds automation, control, and period-end tasks within normal day-to-day activities, allowing the accounting calendar to more closely mirror the daily cadence of the business. Tasks such as reconciliations or intercompany eliminations are embedded in day-to-day activities, empowering finance to close the books more quickly and with greater accuracy.
Indeed, the benefits of continuous accounting can be felt beyond faster close periods and fewer late nights for the accounting and finance teams. A faster — and smarter — accounting process can deliver more accurate and actionable information to leadership earlier, freeing up valuable time for practitioners to contribute more strategically to the business. The faculty to evaluate data integrity at any point in the finance life cycle also allows for continuous monitoring for errors, fraud, and inefficiencies.
When process automation eliminates routine and manual tasks, efficiency and accuracy are the results. Continuous accounting empowers accounting and finance teams to serve as proactive business leaders instead of reactive gatekeepers. For the CFO and the finance team, less time spent on closing the books means more time providing value-added analysis that the business can use to make key decisions.
Process automation is a critical enabler to keep pace with change and elevate the strategic role of finance. Workflow automation powered by RPA and ML can systematize routine, manual tasks like copy-and-paste, data gathering and consolidation, and data entry from one application to another.
Manual processes across procure-to-pay, order-to-cash, and record-to-report cycles can also be automated with RPA solutions. These manual processes are most common where tasks are required between systems, such as keying and rekeying information from one application to another, logging into supplier portals to gather information, or manually updating financial data from Excel spreadsheets.
1. Procure-to-pay automation
Supplier onboarding: Many companies have invoice portals that streamline electronic invoicing, but onboarding new suppliers is still a highly manual process. RPA can help users onboard new suppliers and streamline reporting of credit scores, VAT, and other supplier invoice data.
Portal queries: Most organizations work with suppliers via portals that don’t have a well-defined integration with their ERP system of record. Rather than retaining a staff member to log in and out and copy and paste data, RPA can help practitioners leverage APIs to connect portals and automatically gather or post information as part of a defined workflow.
Price comparisons: Customers often work with a number of suppliers to source a specific raw material or finished product with fluctuating prices. RPA and ML can track changes and report the best pricing automatically without manual intervention.
2. Quote-to-cash automation
Supplier pricing comparisons: When preparing a customer quote, comparing your suppliers’ prices is often a time-sensitive and extremely manual process. RPA can search the supply chain network to deliver the most competitive price for goods and services.
Order exception processing: Sales orders often have terms such as holding the price constant for a number of days. RPA can automatically do a price check to verify the current price against a purchase order.
Delivery reconciliation: Delivery notes are typically manually reconciled with purchase orders to validate customer orders against shipments. RPA can check and approve all matching orders and only notify a human when there is a rule exception.
Customer onboarding and master data management (MDM): As with suppliers in the procure-to-pay cycle, new customers must also be vetted and onboarded, and their data periodically validated and updated. RPA can systematize the MDM process with intelligent managed workflows.
3. Record-to-report automation
Supporting financial close: The financial close and reporting process encompasses all of the tasks and processes — from closing out subledgers to creating and delivering financial filings to regulatory bodies — which involves many systems, departments, and stakeholders. Streamlining the process with predefined automation rules throughout the month can greatly increase data integrity.
Data extraction for accounting close: Departments and divisions company-wide record transactions in journals, which need to be consolidated and reconciled. RPA can automatically gather and consolidate transactions and reconcile them in the ERP system of record.
Data management: Aggregating and analyzing financial and operational performance is a business-critical function, but collecting, processing, and delivering that information to the business in a timely manner is often a manual challenge. RPA can simplify the data management process by managing business processes, including:
- Balance sheet account reconciliations;
- Bank and credit card reconciliations;
- Journal entry creation;
- Inventory reconciliation;
- Cost allocations;
- Revenue recognition;
- Labor reconciliations;
- Controls verification; and
- Amortization and depreciation.
A continuous accounting framework fueled by RPA and ML can routinize accounting tasks and liberate your accounting team from mundane, error-prone work. These enabling technologies will augment the role of modern accounting and finance professionals and empower them to focus their time, creativity, and innovation on business growth.
