Intelligent Automation in Finance & Accounting: Guide for 2024

Managing finance and accounting processes can be complex and labor-intensive. To avoid dangerous financial mishaps that can threaten the stability of a company, manual errors should be minimized.

By combining artificial intelligence (AI) and robotic process automation (RPA), intelligent automation can help businesses automate end-to-end finance processes, thereby increasing their efficiency, lowering error possibilities, and allowing staff to spend their time on more value-added tasks. 

In this article, we’ll explore 4 use cases for intelligent automation in finance and accounting with 3 example case studies.

1. Accounts Receivable & Accounts Payable

Managing accounts receivable (AR) and payable (AP) processes manually is inefficient, error-prone, and can result in delayed payments to vendors, liquidity shortages through short term miscalculations, and dissatisfied employees because of delayed invoice clearances. 

AI-powered intelligent bots can:

• For AP processes:
  • Keep track of vendor data and transactions,
  • Capture data from invoices of different formats with intelligent document processing,
  • Cross-check invoices against purchase orders,
  • Route documents to relevant staff for exception handling,
  • Check for fraudulent documents.
•  For AR processes:
  • Auto-generate invoices,
  • Keep track of days-sales-outstanding (DSO),
  • Send reminders to customers on the due date in the form of emails with attached payment links
  • Process payments
  • Automatically reconcile the balance sheets once payments have been received. 

You can also check our articles on accounts payable (AP) automation and accounts receivable (AR) automation.

2. Intercompany Reconciliations (ICR)

There were over 500,000 mergers and acquisitions (M&A) worldwide between 2010 and 2021, and the announced deals were over 62,000 in 2021, a 24% increase from 2020. Accurate and transparent accounting between subsidiaries is important but the process involves manual tasks such as data entry, extraction, and cross-checking. Intelligent bots can:

• Extract and consolidate transactions data from different business systems,
• Search for related statements in ERP systems,
• Compare balances and send alerts to relevant staff in case of discrepancies,
• Create journal entries.

Feel free to check our article on intercompany accounting automation.

3. Reporting

Financial departments prepare several reports regularly, such as quarterly financial statements or profit and loss statements. Error-free and on-time reporting is critical since these reports indicate an organization’s market status and compliance with regulations, as well as serve as a basis for its forecasts. NLP-powered intelligent bots can:

• Identify and collect business data from business systems and documents,
• Input collected data into designated documents,
• Perform necessary manipulations to data,
• Generate reports, save them, and send them to relevant staff.

We have articles on financial reporting automation and RPA for reporting. Feel free to check.

4. Financial Planning & Analysis

58% of midsize and large companies manage their financial planning and budgeting processes using spreadsheets, but 41% say spreadsheets are not capable of handling their data volumes. Automating the FP&A process can provide businesses with faster forecasts, increased visibility, and better decision-making. Intelligent bots can:

• Aggregate and consolidate data,
• Analyze trends in the market,
• Perform forecast analysis,
• Compare results with forecasts to improve forecast accuracy. 

Case studies

Feel free to read our article on intelligent automation case studies. Some example case studies include:

Tech Mahindra

Problem: Tech Mahindra is a technology service and consulting company with customers around the globe. The company had 15 full-time employees that manually process thousands of invoices with more than a thousand different formats each month.

Solution: The company implemented an intelligent automation solution to automate extracting data from invoices, classifying data, and updating the relevant business systems.

Result: The company reduced the average handle time of invoices by 85% and saved more than 19000 hours annually.¹

Avinor

Problem: Avinor is a state-owned Norwegian airport operator with 3000 employees. The company serves over 50 million passengers and processes more than 100,000 invoices each year. They were reviewing, booking, and approving these invoices almost manually, which is error-prone and repetitive.

Solution: The company adopted an intelligent automation solution for its finance and accounting processes. For instance, for invoice processing, the bot checks all incoming invoices and books them into company systems. When an invoice is ready for approval, the bot reads relevant data fields to send it to the correct personnel.

Result: The bot helps process 100,000 invoices each year, speeding up processes by 90% on average.²

For more on intelligent automation

Feel free to explore intelligent automation use cases with our comprehensive article.

If you want to implement intelligent automation in your business, we have a data-driven list of intelligent automation solution providers. If you have other questions, we can help:

Sources

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