7 Common Accounting Errors & How AI Detects Them (With Examples)

Introduction

It’s a known fact that the entire financial decision-making of an organization depends on the accuracy and completeness of its books. While the world sees CEOs and CFOs make announcements about investments and release financial statements for the public, it is the accountants who are responsible for crossing the T’s and dotting the I’s.

However, the most seasoned accountants can make accounting mistakes – they’re human after all. Overlooking one instance of duplicate payment or missed receipt can cause havoc in the books – leading to financial decisions made based on inaccurate data.

In this blog, we help accounting professionals understand exactly how technologies like Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the world of accounting by automating the detection and resolution of accounting errors and omissions.

What are accounting errors?

Accounting errors and omissions (anomalies) refer to mistakes or inaccuracies made during the process of recording and reporting financial transactions. These errors can occur due to various reasons, such as human error, technological glitches, or incorrect application of accounting principles and standards.

Let’s say, an accountant at Apollo comes across an anomaly (error and/or omission) while reconciling transactions for the month. The next step would be to reach out to the Accounts Receivable (AR) or Accounts Payable (AP) team for supporting documents to investigate the cause of the anomaly. Once they determine the cause, they might have to make a new journal entry to adjust the General Ledger (GL) balance manually in their ERP. As you know, this process typically takes a couple of days and numerous emails. 

Let’s imagine the same scenario with an AI-powered anomaly management solution. 

Since it integrates with your ERPs, CRMs, and banks, the data automatically flows into the system. It looks for patterns in your historical data (2 to 3 years) and uses its rules and algorithms to provide a ready-to-act list of errors and omissions as ‘tasks.’

When you open an anomaly task, you can find all the relevant transactions and data to help you investigate. It also gives you a space to attach evidence that would later help with internal and external audits. You can make journal entries in the system itself after which they’ll be automatically posted to your ERP.

Now, let’s see how AI algorithms and rules help you identify and resolve anomalies in real-life.

7 Real-Life Accounting Anomalies Examples & How AI Detects Them

Accounting anomalies examples can range from errors like recording a transaction under the wrong GL account or omissions like forgetting to record a recurring transaction. Let’s see how AI studies the patterns in previous transactions and flags the exceptions.

Errors of Commission

Errors in this category can occur due to incorrect actions, such as recording a transaction with an incorrect general ledger account number or using an improperly rounded or miscalculated value. Additionally, errors of commission can include reversed entries where debits and credits are swapped or duplicated entries.

Example 1: Vendor & GL Account Mismatch

An expense with the vendor Apollo has been posted to Gl Account for Office Expenses (GL account 6670). However, in the last 12 months, all expenses for Apollo were posted to the GL Account for Software Licenses (GL account 6215).

Example 2: Legal Entity & GL Account Mismatch

At Apollo Industries USA, 401K-related transactions are always captured in the GL Account 2260. However, in the current period, there is an entry created with a combination of Legal entity Apollo Industries Mexico and GL account 2260 – which has never occurred in the past.

Example 3: Legal Entity & Department Mismatch

An expense has been recorded in GL account 5126 for Employee meal vouchers in the Legal entity Apollo Industries USA. However, the department mentioned in the entry is “Quality Assurance” which is unusual as this department is normally not used under the Legal Entity Apollo Industries USA but always under Apollo Industries Mexico.

Example 4: Unexpected Debit/Credit Transaction

GL Account 7005 is an interest expense-related GL account and GL Account 7002 is an income account that tracks all the interest income. A new entry for $200 has been posted to GL account 7005 as a Credit entry instead of being recorded as an income in GL account 7002 with a Debit entry. This implies an income-related transaction (Credit) has been posted into an Expense related GL Account (Debit)which is an anomaly.

Example 5: Recurring Time Deviations

A recurring bill payment to Apollo is usually posted under the GL Account 2005 by the first of every month against subscription fees. However, for the current month, it is recorded on the 29th April instead.

Errors of omission

Missing transactions can result in errors in the accounting records, where a transaction is overlooked or not properly recorded.

Example 6: Accrual Reversals Missed

A vendor accrual calculated for unpaid invoices year-to-date (YTD) February and posted to the GL Account 2100 for Other Accrued liability has not been reversed in the month of March. Ideally, this entry should have been reversed before the revised Vendor accrual for YTD March is calculated and posted.

Example 7: Recurring Transaction Missed

A rent expense that is usually recorded under the GL Account 5700 for Rent & Lease Expense between the 3rd and 7th of each month. However, this has not been recorded at all for the entire current month.

Mitigate accounting errors with HighRadius’ automated anomaly detection software

HighRadius’ AI-powered Anomaly Management software helps you detect and resolve errors and omissions quickly to achieve day zero month end close.

Real-time anomaly detection: Get alerts for potential anomalies based on AI capability to analyze data, establish patterns, and identify deviations that could indicate an anomaly

AI-powered alerts for potential anomalies: Get alerts for potential errors and omissions based on AI capability to analyze data, establish patterns and identify deviations that could indicate an anomaly

Better visibility of all anomalies and transactions: A single source of truth for all transactions and customized view of anomalies to enable keeping track of team’s progress during each financial close cycle

Improved compliance and auditability: Viewing all anomaly attributes in one place helps auditors find the right information and support documents for ensuring the accuracy of financial statements

Defined workflows for better collaboration and efficiency: When an anomaly requires some research in order to make a correction, you can create a task and add it within a close workflow process so it doesn’t get lost in emails

FAQs

1. What are the common types of errors in accounting?

   In accounting, errors can occur due to a variety of reasons, ranging from simple mistakes to more complex issues. Some common accounting errors are omission errors, reversal errors, and input errors.

2. How can accounting errors be corrected?

   After identifying the type of error, accountants either make adjusting journal entries or record reversing entries. Further, they communicate the correction and review and reconcile it.

3. How do you find errors in financial statements?

   Accountants typically find errors while manually reconciling transactions to balance the accounts for month-end closing. AI-based anomaly detection helps find errors automatically based on historical data patterns so that accountants can save time and focus on resolving anomalies quickly.

Conclusion

An AI-powered anomaly solution not only helps you catch errors in time but also resolves them efficiently with task assignment and prioritization.