In 2024, Gartner surveyed 497 financial control professionals across the United States, the United Kingdom, Singapore and Australia. The finding is clear: 59% of accountants make multiple financial errors each month, and 18% make at least one every day. This isn't a skills problem — it's the direct result of data volumes, tight deadlines and manual processes that remain widespread in accounting firms and SMEs.
Artificial intelligence is changing the equation. Anomaly detection tools, automated reconciliation and real-time controls can now drastically reduce these errors — not by replacing the accountant, but by freeing them from repetitive checks so they can focus on analysis and advisory work.
In this article, you'll see why errors are so common, how AI detects them, what types of anomalies it targets, and how to deploy a verification tool suited to your organisation.
Key Takeaways
- In 2024, 59% of accountants make multiple financial errors per month, according to Gartner (497 professionals surveyed).
- Organisations that adopt the right technology reduce their errors by 75% — same Gartner source.
- In 2025, 34% of finance teams using AI do so for error and anomaly detection (Gartner, 183 CFOs).
- AI embedded in cloud ERPs will enable accounts to close 30% faster by 2028 (Gartner, February 2026).
Why Are Accounting Errors So Common in 2026?
In February 2024, Gartner published the results of a survey of 497 financial control professionals: 33% make multiple errors per week and 18% make at least one every day (Gartner, Accountants Make Several Financial Errors Per Week Due to Capacity Constraints, 21 February 2024). This isn't an anomaly — it's the norm in high-volume manual data entry environments.
Three structural factors explain this frequency:
- Data volume: a 10-person SME can process hundreds of invoices, expense claims and bank entries each month. At that rate, even a 1% error rate produces several significant anomalies per period.
- Data entry fatigue: academic research establishes an error rate of 1 to 4% per field for manual entry, depending on skill level and data complexity (Barchard & Pace, Behavior Research Methods, 2011). Even an experienced operator remains at risk.
- System fragmentation: when invoicing, payroll, CRM and banking aren't connected, manual re-keying between tools multiplies risk at every data transfer.
The good news: the same Gartner study shows that organisations that deploy technology which is easy to use and well adopted by teams reduce their financial errors by 75%. AI isn't a magic wand — it's a precise automation tool targeting high-risk areas.
How Does AI Detect Anomalies in Your Accounting Records?
In November 2025, Gartner surveyed 183 CFOs: 34% of finance teams using AI do so specifically for error and anomaly detection, making it the third most common use case, behind knowledge management (49%) and accounts payable automation (37%) (Gartner, Finance AI Adoption Survey 2025, November 2025). Here's how these systems work.
Intelligent Document Recognition
AI automatically extracts data from PDF invoices, scans and emails. Unlike traditional OCR, models trained on millions of documents recognise variable structures and validate each extracted data point against business rules: amounts consistent with supplier history, correct VAT rate, existing customer reference.
Anomaly Detection Through Machine Learning
Machine learning models learn the normal patterns of your entries: typical amounts by supplier, payment frequency, value ranges for each account in your chart of accounts. Any entry that deviates from these patterns triggers a priority alert. This is more precise than a fixed rule: the system adapts to your specific activity.
Automated Bank Reconciliation
AI compares bank transactions against accounting entries in real time and flags discrepancies immediately. What used to take several hours at month-end now happens continuously, catching duplicates, unrecorded payments and incorrect amounts as they occur.
What Types of Accounting Errors Does AI Catch Automatically?
According to Gartner (2024), 75% of accounting errors disappear when you deploy the right technology with strong team adoption. AI verification isn't a spell-checker: it understands the logic of financial data. Here are the six categories of anomalies most commonly intercepted.
1. Duplicate Entries
An invoice received twice, a payment recorded in duplicate: AI detects exact or near-exact duplicates — same supplier, same amount, close dates — that the human eye misses during a quick check under pressure.
2. Entries Posted to the Wrong Account
A maintenance charge allocated to raw materials, a training expense recorded as travel costs: AI learns the structure of your accounts and flags atypical allocations at the point of entry.
3. VAT Errors
Incorrect rate applied, wrong calculation base, non-recoverable VAT deducted in error: tax rules are complex and change regularly. AI applies the current tax framework to each entry and generates an alert before the return is filed — not after.
4. Missing Entries
A recurring monthly charge not recorded, a provision missed at period close: AI compares the current month against comparable periods and alerts you if a usual expense category doesn't appear within the expected window.
5. Unusual Amounts
An invoice ten times higher than the historical average for that supplier, an expense reimbursement outside policy: AI sets dynamic thresholds and escalates cases that fall outside your activity's normal limits.
6. Date Inconsistencies
A December invoice posted in January crosses the financial year boundary and distorts your results. AI checks consistency between invoice date, accounting date and payment date, and flags suspicious discrepancies.
Field observation: In the SMEs we work with in Morocco, VAT errors and duplicate entries consistently account for more than 60% of anomalies detected during initial AI audits. These two categories are also the most costly in terms of tax penalties if not corrected before the return is filed.
What Concrete Time Savings Can You Expect from Accounting AI?
In February 2026, Gartner predicted that AI embedded in cloud ERPs will enable a 30% faster financial close by 2028 (Gartner, Embedded AI in Cloud ERP Applications Will Drive a 30% Faster Financial Close by 2028, 24 February 2026). This aligns with real-world results: in January 2026, Deloitte reported that 66% of organisations see measurable productivity and efficiency gains after adopting AI (Deloitte, State of AI in the Enterprise 2026, 3,235 executives).
Here's a concrete comparison for a mid-sized firm or SME:
| Accounting task | Manual time | Time with AI | Saving |
|---|---|---|---|
| Monthly bank reconciliation | 4 to 8 hours | 30 to 45 min (alert review) | ~85% |
| Duplicate detection (500 entries) | 2 to 3 hours | Automatic in real time | ~95% |
| VAT check before filing | 3 to 5 hours | 15 to 30 min (alert review) | ~80% |
| Full monthly close | 5 to 8 days | 3 to 5 days | 30 to 40% |
How Do You Deploy an AI Verification Tool in Your Firm or SME?
In May 2026, KPMG published its global report on AI in finance (1,013 financial executives, 20 countries): 74% of organisations believe the ROI of their AI deployment meets or exceeds expectations (KPMG, Global AI in Finance Report 2026). Success depends on a structured five-step approach.
Step 1: Audit Your Current Error Hotspots
Before choosing a tool, identify where anomalies occur most frequently in your organisation. Analyse your bank rejections, VAT return corrections and closing discrepancies over the past six months. This data will guide AI configuration and establish a baseline for measuring progress.
Step 2: Start with a High-Impact Task
Don't try to automate everything at once. Start with the most time-consuming and error-prone process — often bank reconciliation or supplier invoice verification. A phased deployment ensures better adoption rates and visible results from month one.
Step 3: Integrate AI with Your Existing Accounting Software
Modern tools integrate via API with Sage, QuickBooks, EBP, Odoo or your ERP. Check compatibility before purchasing and make sure synchronisation is bidirectional: AI needs to both read and write to your system of record, not manage a parallel copy.
Step 4: Train Your Team to Manage Alerts
AI generates alerts: someone needs to review them and decide. Training your staff to distinguish a false alarm from a genuine anomaly is as important as the technical deployment itself. Allow two to four weeks for the team to get up to speed.
Step 5: Measure and Fine-Tune Thresholds
After the first month in production, analyse the ratio of false alerts to genuine errors detected. A well-calibrated system targets fewer than 10% false positives. Adjust rules and thresholds based on your real activity — a firm specialising in trading has different patterns from a consultancy.
Ready to Reduce Accounting Errors in Your Business?
EasyDigia helps SMEs and accounting firms implement AI verification tools tailored to their activity — automated reconciliation, anomaly detection, VAT controls. Our solutions integrate with your existing software without disrupting your teams.
Frequently Asked Questions About AI and Accounting Verification
- Can AI replace the accountant for error checking?
- No. AI automates anomaly detection, but the final decision remains human. In 2025, 34% of finance teams use AI for error detection (Gartner, 183 CFOs) — always under human supervision. AI frees the accountant from repetitive checks so they can focus on higher-value work.
- What's the difference between standard accounting software and an AI verification tool?
- Standard accounting software records transactions and generates reports. An AI tool actively analyses data, learns the normal patterns of your activity and proactively flags discrepancies. It's the difference between a passive ledger and an active controller that never sleeps.
- How long does it take to deploy an AI accounting verification tool?
- A minimal deployment — bank reconciliation and duplicate detection — takes two to four weeks: one week of configuration, two to three weeks calibrating alerts. A full deployment covering VAT, account allocations and period close requires two to three months including team training.
- Are AI accounting verification tools accessible to SMEs?
- Yes. Modern SaaS solutions start at a few dozen euros per month. In May 2026, KPMG reported that 74% of organisations using AI in finance see a positive ROI — including mid-sized firms (KPMG, Global AI in Finance 2026, 1,013 organisations, 20 countries).
- What errors can't AI detect?
- AI can't detect errors of judgement: undervaluing a provision, misinterpreting a complex tax rule, or choosing an accounting method that doesn't suit the situation. These decisions require the expertise and context of a qualified professional that AI complements, not replaces.
Conclusion
Accounting errors aren't a skills issue — they're the mechanical result of data volumes and deadline pressure. The Gartner data is unambiguous: 59% of accountants make errors every month, and 75% of those errors disappear when you deploy the right technology.
AI verification isn't a replacement for the expert: it's a safety net that intercepts anomalies before they become tax problems or costly closing discrepancies. For SMEs and firms processing hundreds of transactions each month, it's one of the most cost-effective investments available — with 74% of organisations confirming a positive ROI in the first year (KPMG, 2026).
The next step? Identify the highest-risk process in your organisation and start there. Contact EasyDigia for a free audit of your error hotspots — no commitment required.
Sources
- Gartner, Gartner Survey Shows That a Third of Accountants Make Several Financial Errors Per Week Due to Capacity Constraints, 21 February 2024. gartner.com — retrieved 14 July 2026.
- Gartner, Gartner Survey Shows Finance AI Adoption Remains Steady in 2025, 18 November 2025. gartner.com — retrieved 14 July 2026.
- Gartner, Gartner Predicts Embedded AI in Cloud ERP Applications Will Drive a 30% Faster Financial Close by 2028, 24 February 2026. gartner.com — retrieved 14 July 2026.
- KPMG, Global AI in Finance Report 2026, May 2026. kpmg.com — retrieved 14 July 2026.
- Deloitte, State of AI in the Enterprise 2026, January 2026. deloitte.com — retrieved 14 July 2026.
- Barchard, K. A. & Pace, L. A., Preventing Human Errors: The Impact of Data Entry Methods on Data Accuracy and Statistical Results, Behavior Research Methods, 2011.
- Photos: Unsplash — free licence, attribution required. unsplash.com