AI & Automation in Accounting: What's Your Excuse for Not Implementing it in 2024?

Why crunch numbers when you can crunch time? Discover how AI & Automation in 2024 are transforming the accounting function of finance from transactional to analytical!

For this article, we look back into one of our conversations—which we found extremely relevant for today's accountants—with Abhishek Gupta, Executive Director at EY, who has more than 20 years of experience in the industry transforming Fortune 500 accounting practices.

Albert Einstein once said, “the measure of intelligence is the ability to change”. This quote perfectly captures why today’s accounting teams must transform.

Firstly, over 50% of finance tasks are repetitive, often due to siloed systems and rapid organizational growth outpacing finance functions. This results in adding more personnel while clinging to outdated processes, which isn’t simply scalable.

Secondly, not all organizations have the budget or appetite for advanced technologies that could streamline these processes.

Let’s trace back to the roots of the problem!

Fewer than 10% of activities in Accounting are dedicated to analysis and action

Finance professionals often lack real-time insights because they spend too much time on manual tasks like general ledger entries and account reconciliations. 

This focus on routine tasks prevents them from identifying the root causes of critical issues, such as unmatched balances, data inconsistencies across systems, or discrepancies in financial statements. As a result, analysis and action account for less than 10% of accounting activities, limiting the accounting team’s ability to provide strategic input.

While rushing to complete their close tasks, accounting teams rarely get any time to focus on understanding the data and recognizing patterns to support the bigger financial decisions taken by CFOs and investors. By the time they share their financial reports, the information is already stale and it’s time to start another close cycle.

Because manual tasks take up so much time, companies are quickly adopting AI and automation technologies in finance. Over the past decade, these technologies have greatly improved, solving many business problems that finance teams have faced for a long time.

As we look at the journey towards Autonomous Finance, we’ll see how moving from simple desktop automations to advanced AI solutions is helping finance professionals shift their focus from repetitive tasks to strategic analysis.

Also Read: Is Record-to-Report Automation Threatening 20% of Finance Roles? (link)

How has the Autonomous Finance Journey transformed over the last decade?

You can’t do anything in finance without technology now. That’s a fact. It’s new-gen finance with the help of which finance teams are evolving.

The first phase: Desktop-based automation

Many excel-based processes were primarily meant for someone actually doing the processes on their local desktop. This aimed to make work easier by using simple automations like Excel functions such as VLOOKUP and macros—these are basic automations.

RPAs (Robotic Process Automations) took it one step ahead

With RPAs, we saw many players entering the market, providing solutions from an overall finance function perspective. This wasn’t just dedicated to small desktop-based automation but was function-based. 

In finance, subprocesses like invoice processing, month-end closing, and billing often involve repetitive tasks. Initially, automation solutions used screen scraping technology to mimic human actions in ERP systems. 

For example, RPA could move data between applications by imitating clicks. However, RPA had a major limitation: it couldn’t analyze unstructured data and lacked intelligence.

IPAs (Intelligent Process Automation): The next evolution

IPA, a more advanced solution, could process unstructured data.

Imagine a finance team that needs to analyze expense reports from various departments. These reports are often in different formats, such as PDFs, emails, and spreadsheets.

Without IPA:

• A finance professional manually reviews each document, extracts relevant data, and enters it into a central system. This process is time-consuming and prone to errors.

With IPA:

• IPA tools automatically scan and process these unstructured documents.
• They extract key data points like dates, amounts, and categories based on predefined parameters.
• The system then consolidates this information into a unified database, ready for analysis.

Hyper Automation & GenAI: Transcend

The adoption curve became steeper, almost exponential, as organizations rapidly moved from IPA to Hyper Automation and GenAI. While the transition from desktop-based automation to IPA was gradual, the shift from IPA to GenAI has been remarkably fast.

Hyper automation takes IPA a step further with predictive analytics. It processes historical data to forecast future trends, incorporating both internal organizational data and external factors like micro and macroeconomic indicators, demographics, and potential disruptive events (such as COVID-19) to predict industry forecasts.

GenAI generates recommendations, commentary, and text. Unlike previous technologies, it’s not just data-based but also text-based. For example, when preparing quarterly 10-K reports in the US, extensive commentary is required to justify the numbers. 

GenAI can analyze the data, create the complete report based on the data in your ERP system, and generate reasoning for trends—like explaining quarter-over-quarter sales increases. This eliminates the need for analysts to manually analyze data and provide commentary.

How AI & Automation are transforming the Financial Close Process in Accounting

Two areas where companies today are looking to improve their Record-to-Report (R2R) process using AI are cost and accuracy in the month-end closing process. 

This includes reducing audit observations, reducing manual errors in reporting, and decreasing the cycle time of the month-end close process.

According to IBM Institute of Business Value:

1. Organizations that have automated more than 95% of their journal entries have 38% lower cost per journal entry
2. Organizations that have fully implemented AI for finance operations process journal entries 66% faster

Having explored how AI and automation are transforming the financial close process, let's examine a real-world example that illustrates the benefits of streamlining the month-end close. 

This case study demonstrates how these technological advancements can be applied in practice to address common challenges in financial operations.

Case Study: Streamlining month-end close for an Anglo-Swedish Pharma Giant

An Anglo-Swedish multinational pharmaceutical and biopharmaceutical company faced similar challenges. Their month-end close cycle time was around workday 9, indicating an inefficient process.

They had to perform numerous manual journal entries in their system, and errors often occurred. For example, invoices frequently failed to pass through the subledger system, requiring manual information entry.

Let's dive deeper into their problem statement before exploring the solution EY provided and the resulting outcomes.

Problem Statement

1. Disconnected systems and processes: The company lacked integrated systems and was utilizing multiple ERP systems, mainframe and legacy systems, and flat files. Collecting and connecting this siloed data was challenging, resulting in numerous manual journal entries in various formats across different systems.
2. Time-consuming reconciliation process: Reconciliation was another major challenge. For instance, reconciling vendor payments from bank statements with ERP system records was particularly difficult and time-intensive.

Solution

1. Standardized Journal Entry Reporting: They first standardized journal entry reporting, which structured the data. Then, they automated the data collection process from the source using a bot. This bot automatically validates all entries in the journal entry template submitted by requestors.
2. Automated Reconciliation: They also automated bank statement reconciliation. A bot now downloads structured bank statements, uses OCR technology to extract information, and reconciles the data—eliminating manual intervention.

Results

• They managed to slash manual efforts (manual journal entries) by half.
• Achieved a 90% improvement in journal automation.
• Reduced manual certification by more than half, with a 75% automation rate.
• Shaved off 5 whole days from their close cycle time.
• Now completing assignments on time 85% of the time.
• Achieved financial harmony by eliminating intercompany reconciliation issues.

Case Study: Streamlining Month-End Close for a British multinational consumer goods company

This British multinational, with a revenue of £16B and around 61,000 employees, was growing rapidly and launching numerous products. The finance organization needed to keep pace and provide enhanced business partnership and analytics for faster, better decision-making.

Problem Statement:

• Lack of standardization in the global month-end closing checklist, with each country following a different timetable.
• Highly manual Record-to-Report (R2R) process (~97% manual activities) with under-optimized use of technology and automation.
• Issues with controls, data quality, and non-standard processes.
• No formal journal entry approval process, with approvals done via emails.
• Significant number of journals with immaterial amounts posted during month-end closing, leading to time spent on non-value-adding activities.

Solution:

• Standardized Journal Entry Reporting: EY standardized journal entry reporting and automated data collection using a bot, which validated all entries in the journal entry template submitted by requestors.
• Automated Reconciliation: EY automated bank statement reconciliation. A bot downloaded structured bank statements, used OCR technology to extract information, and reconciled the data, eliminating manual intervention.

Results:

• 10% manual journal entry line-item percentage.
• 40% of total accruals generated using a fully automated method.
• 33% risk reduction.
• 11.5 days to produce period-end management reports.
• 5.5 days to complete the monthly consolidated financial statements.

Why haven’t you embraced AI & Automation in your accounting process in 2024?

Well... we don't want to sound outright dismissive about why many organizations still haven't become part of the AI revolution. There are many constraints such as cost, skill gaps, technology limitations, and use case challenges.

However, these shouldn’t be the excuses for not finding a solution that best addresses your concerns in accounting procedures. 

In short, the time is now!