How to Use AI to Verify Manual Journal Entries?

Manual journal entries are a necessary component of accounting, used to record everything from accruals and deferrals to complex, non-standard transactions. But every time an entry is keyed in by hand, a window opens for human error, compliance breaches, or even outright fraud. This article will show you how AI is transforming the review process, offering a faster and more reliable way to verify manual journal entries and fortify your financial controls.

Why Manual Journal Entries Are a Minefield

Modern accounting systems automate a majority of transactions, but manual journal entries (MJEs) remain the method for handling adjustments, corrections, and unique financial events. While unavoidable, they carry inherent risks that even the most meticulous review processes can miss. Understanding these specific dangers is the first step toward appreciating what AI-powered solutions can do.

The most common risk is simple human error. An accountant might transpose digits, debit the wrong account, or post an entry in the incorrect period. A late-night, deadline-driven closing process only magnifies this risk. A single misplaced decimal point in an accrual entry can misstate earnings and lead to painful corrections down the line. Similarly, posting revenue to a miscellaneous G/L account instead of the correct revenue account can skew financial analysis and performance metrics.

Beyond innocent mistakes, MJEs are a classic vehicle for financial fraud. Because they bypass many of the automated controls that govern routine transactions, they can be used to conceal theft or manipulate financial results. An employee might create a bogus entry to a clearing account that they later empty, or a manager might post a large, round-number entry on the last day of the quarter to artificially inflate revenue and hit a bonus target. These entries often look legitimate on the surface, making them difficult to spot in a sea of thousands of transactions.

Finally, the sheer volume of MJEs during a period close makes a comprehensive manual review nearly impossible. Teams are forced to rely on sampling—reviewing only a selection of entries based on a monetary threshold or random selection. This approach is better than nothing, but it's fundamentally a game of chance. You might review 50 entries and miss the one problematic entry that falls just below your materiality threshold. This process is both time-consuming and dangerously incomplete.

The Limits of Traditional Journal Entry Review

The standard MJE review process has been the bedrock of internal controls for decades. Typically, it involves a multi-layered approval workflow. A staff accountant prepares the entry, a senior accountant or manager reviews it for accuracy and proper documentation, and in some larger organizations, a controller gives a final sign-off. Auditors then conduct their own testing, usually by pulling a sample and tracing it back to supporting documents.

This process offers a reasonable check on individual entries, but it has significant limitations:

• It's Slow and Labor-Intensive: During the month-end close, every hour counts. Manual reviews create bottlenecks, forcing senior staff to spend their time verifying data entry instead of performing high-level analysis. The pressure to close the books can lead to rushed or superficial reviews.
• Sampling is Inherently Flawed: Auditors and internal reviewers simply cannot examine 100% of MJEs. An MJE-based fraud scheme is often designed to fly under the radar by spreading small amounts across multiple entries or keeping transactions just below the established review threshold. Sampling will almost certainly miss these schemes.
• It Lacks Context: A human reviewer checking a single entry sees it in isolation. They might confirm the debit matches the credit and that there’s a short description, but they lack the ability to see the bigger picture. Is this entry out of character for the person who posted it? Is the amount unusual for this G/L account at this time of month? Answering these questions manually would require hours of forensic-level digging for every entry.

The traditional process catches the obvious errors, but it struggles with subtlety, scale, and context. It’s a net with holes big enough for significant problems to slip through.

How AI Changes the Game for Journal Entry Verification

Artificial intelligence isn't about replacing accountants; it's about equipping them with tools that can see what a human simply cannot. By processing massive volumes of data in seconds, AI algorithms can identify questionable entries based on patterns, outliers, and learned behaviors. This turns the review process from a sampling-based guessing game into a comprehensive, risk-based analysis.

Anomaly and Outlier Detection

This is one of the most powerful applications of AI in accounting. Anomaly detection systems analyze every single journal entry and compare it against historical data and established patterns. The system flags entries that deviate from the norm, such as:

• Unusual Timing: Entries posted on a weekend, late at night, or on a holiday. While sometimes legitimate, these can be a red flag for fraud or unauthorized activity.
• Questionable Posters: An entry posted by someone outside the finance department or by an employee who doesn’t typically create that type of entry. For example, if a P-card administrator suddenly posts an MJE to a revenue accrual account.
• Atypical Amounts: Entries with unusually high values for a specific account or round numbers (e.g., exactly $50,000) that are often used to manipulate figures. It can also flag exceptionally small entries used to test system controls.
• Strange Descriptions: Entries with generic or missing descriptions, or descriptions matching those used in previous fraudulent activities.

Pattern Recognition and Rules-Based Checks

AI is exceptionally good at identifying patterns over thousands of transactions. It can learn what "normal looks like" for your business and automatically enforce rules that are difficult for humans to monitor at scale.

For example, an AI could be configured to automatically flag:

• Duplicate Entries: Exact or highly similar entries posted within a short timeframe.
• Entries to High-Risk Accounts: Every MJE that debits or credits a suspense, clearing, or miscellaneous reserve account. These accounts are frequently used to temporarily hide fraudulent transactions.
• Weekend G/L Postings: An entry with an effective date that falls on a Saturday or Sunday, which circumvents many daily-run controls.
• Violations of G/L Account Combinations: Some transactions should only ever involve certain account pairs. An AI can learn these pairings and flag an entry that debits Accounts Receivable while crediting a non-revenue account, for instance.

Compliance and Internal Policy Enforcement

For publicly traded companies, AI can be a powerful tool for complying with Sarbanes-Oxley (SOX) regulations. Control frameworks can be embedded directly into the AI model. For instance, any journal entry where the person who entered it is also the person who approved it represents a clear violation of Segregation of Duties (SoD). An AI can identify and flag these instances instantly, whereas a manual check might miss them.

A Practical Guide to AI-Powered Journal Entry Review

Integrating AI into your MJE process is an achievable project, not a far-off fantasy. Advanced capabilities are increasingly built into Enterprise Resource Planning (ERP) systems and specialized auditing tools. Here is a clear path to begin.

1. Assess Your Current Process and Identify Risks

Start by flowcharting your existing MJE workflow. Where are the delays? What kinds of errors are most frequently found after the fact? Talk to your accounting team and auditors to identify the highest-risk areas. Is it revenue recognition, inventory reserves, or intercompany transactions? This initial assessment helps you focus your AI implementation on the areas where it will provide the most benefit.

2. Explore AI-Enabled Accounting and Audit Tools

You don't need to build an AI from scratch. Many established software providers offer these capabilities. Audit analytics platforms like MindBridge specialize in this field, using AI to scan entire general ledgers for risk. Large-scale accounting platforms such as BlackLine have modules dedicated to a risk-based analysis of journal entries. Major ERP systems are also baking these features in; for example, NetSuite offers intelligent workflows and SAP S/4HANA has machine learning capabilities to detect financial anomalies. Evaluate these tools based on their ability to integrate with your existing general ledger and the specific risks you identified.

3. Start Small with a Pilot Program

Don't try to boil the ocean. Choose a specific, manageable area for a pilot program. You could start by applying AI review exclusively to entries related to the quarter-end close for a single subsidiary or focusing only on MJEs that affect revenue-related accounts. This gives your team a chance to work with the system, understand the types of exceptions it generates, and fine-tune its parameters without disrupting the entire close process.

4. Define Your Rules and Fine-Tune the Model

Out of the box, an AI system will flag many potential issues. Your team's job is to refine the rules to reduce "false positives." This involves teaching the model what is truly unusual for your specific business. For example, a large, late-night entry might be standard procedure for your international consolidation team but a major red flag for your domestic accounts payable team. You can set specific thresholds (e.g., flag all manual entries over $5,000 to fixed asset accounts) and rules that fit your internal control framework.

5. Train Your Team to Work with the AI

It’s important to position an AI tool as an assistant, not a replacement. Train your accountants on how to interpret the AI's output. Their role shifts from manually checking transactions to investigating the high-risk exceptions that the AI surfaces. This is ultimately more valuable work. They are no longer looking for needles in a haystack; the AI hands them the needles it finds, and their job is to determine why they are there. This elevates their responsibilities from clerical review to genuine financial investigation.

Final Thoughts

Manual journal entries will always present a degree of risk, but you no longer have to rely on slow, incomplete, and error-prone review processes. By using AI to perform a comprehensive analysis of every entry, accounting teams can identify anomalies and enforce controls at a scale and speed that humans simply cannot match.

This focus on using technology to automate confirmation and free up professionals for higher-value analytical thinking extends beyond journal entries. When it comes to complex tax research, the goal is the same: reduce time spent on manual discovery to make more time for strategic advising. This is precisely why we developed Feather AI. Instead of hours spent searching through dated blogs and primary source documents, accountants get instant, citation-backed answers, allowing them to transform that time savings into better client guidance.