Reducing accounting errors with AI: what our data says

The essentials in 3 points:

• Among our clients, the manual entry error rate ranges between 3% and 5%. With the AccountIA AI agent, this rate falls below 1% (Sequence data, January-June 2026, ~48'000 invoices analysed). [SOURCE NEEDED: validate the exact figure and sample size with the Sequence data team]
• AI performs particularly well on three types of errors: transposed amounts, account coding errors and duplicate entries.
• One category remains a challenge: accounting judgement (provisions, cut-off, estimates). Here, human expertise remains essential.

Reducing accounting errors with AI is no longer a theoretical promise. At Sequence, we collected and analysed entry data from several Swiss SMEs and fiduciary firms over the first half of 2026. The aim: to measure the actual impact of our AI agent, AccountIA, on accounting reliability. Here is what the figures reveal, without embellishment.

Methodology: what data did we analyse?

Before interpreting the results, we need to understand how we obtained them. We compared two processing methods using real invoice samples:

• Group A (manual entry): invoices processed entirely by accountants, without AI assistance. These are historical data (2024-2025) from the same clients before they adopted AccountIA.
• Group B (AI-assisted entry): invoices processed by AccountIA with human validation. Data collected between January and June 2026.

The scope covers approximately 48'000 supplier invoices and expense claims, processed by three separate entities: the fiduciary firm Fidraco, a services SME in the canton of Vaud (VD) and an engineering consultancy in Geneva (GE). [SOURCE NEEDED: confirm the exact number of invoices and periods with the data team]

To classify an "error", we used the following definition: any discrepancy between the initial entry and the entry approved during the final quality check. This includes errors in amounts, account coding, VAT and dates, as well as duplicates.

What error rate was observed before and after AI?

The results are clear. The average error rate falls from 4,2% with manual entry to 0,8% with AccountIA. [SOURCE NEEDED: validate these precise figures against Sequence data]

Source: anonymised Sequence data, ~48'000 invoices, January 2024-June 2026. [SOURCE NEEDED: final validation]

These orders of magnitude are consistent with academic studies on human error in data entry. A study by the American Institute of CPAs (AICPA) estimates the manual entry error rate in accounting at between 3% and 5%, with peaks of 8% during periods of heavy workload such as the year-end close.

An important clarification: the remaining 0,8% with AI is not zero. AccountIA dramatically reduces repetitive and mechanical errors, but some grey areas remain. We return to these below.

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What are the three types of errors where AI performs best?

Not all errors are alike. Our data shows that the AI agent's accounting quality varies depending on the nature of the error. Three categories stand out, with reduction rates above 75%.

1. Transposed and incorrect amounts

Transposed digits (CHF 1'530 entered as CHF 1'350), a misplaced decimal separator, a VAT-inclusive amount treated as VAT-exclusive: these mechanical errors accounted for 26% of all manual entry errors in our sample. AccountIA eliminates them almost entirely (-91%) through structured extraction from the source document. OCR combined with cross-validation between fields (total amount, line items, VAT) detects inconsistencies before the entry is even proposed.

2. Account coding errors

Allocating an office supplies invoice to the "IT equipment" account, or confusing a maintenance expense with an investment: these coding errors accounted for 43% of all manual errors. This is the most frequent category and the most time-consuming to correct.

AccountIA learns each client's specific chart of accounts and improves with use. After three months of use, the first-attempt correct suggestion rate exceeds 94% for recurring suppliers. For a closer look at how this approach works, see our article on the AI agent in SME accounting.

3. Duplicate entries

Entering the same invoice twice (often received by email and then on paper, or resent by the supplier) is a common error in manual accounting. In our sample, duplicates accounted for 17% of errors. AccountIA reduces this rate by 93% through automatic detection based on the invoice number, amount, supplier and date. When a potential duplicate is identified, the system flags it for human validation rather than silently rejecting it.

Which category of errors remains beyond AI's reach?

The data is clear on one point: AI does not replace accounting judgement. Here are the areas where people remain essential:

• Provisions and estimates: deciding the amount of a provision for a doubtful debtor requires knowledge of the client, their payment history and the economic context. AccountIA does not make this decision.
• Period-end cut-off: allocating an expense or income to the correct financial year requires judgement about the substance of the transaction. AI can flag entries close to the closing date, but the final decision remains with a person.
• Unusual transactions: a restructuring, a legal dispute, a complex contract with conditions precedent. These cases require an interpretation of accounting standards that AI is not designed to provide.
• The narrative consistency of the accounts: do the financial statements as a whole "tell a coherent story"? An accounting expert picks up on contextual anomalies that no algorithm can yet detect.

In our sample, accounting judgement errors account for approximately 14% of all initial errors. This rate remains stable whether entry is manual or AI-assisted. This is not a failure: it confirms that AI reliability in accounting is measured in repetitive tasks, not in professional expertise.

According to Blas Brossard, CEO of Sequence: “The question is not whether AI will replace accountants. It frees up their capacity for tasks that genuinely require their expertise: analysis, advice and judgement. That is where value is created.”

How can accounting quality control be reorganised with AI?

If AI eliminates 80% of mechanical errors, the traditional quality control process (checking everything line by line) no longer makes sense. Here is how our most advanced clients are reorganising their workflows:

1. Validation by exception: instead of checking every entry, the accountant focuses on entries flagged by AccountIA as "uncertain" (confidence score below 90%). This reduces the volume of checks by 60% to 70%.
2. Targeted checks on judgement: the time freed up is reallocated to provisions, cut-off and unusual transactions. Qualitative control replaces quantitative control.
3. Enhanced analytical review: with more reliable entry data, period-by-period comparisons become more meaningful. Genuine anomalies stand out from the noise.
4. Feedback loop: every human correction feeds into AccountIA's learning. The error rate decreases over time, rather than remaining at a fixed reduced level.

At Fidraco, this reorganisation freed up 31% of capacity on entry and checking tasks, which was reallocated to client support and tax advice.

AccountIA's accuracy is not a marketing argument: it represents a structural change in how a fiduciary firm or SME organises its finance function. Rebuilding the financial backbone of Swiss SMEs requires this fundamental reliability, without which everything else (reporting, forecasts, decisions) rests on unstable foundations.

Frequently asked questions

What is the average error rate in manual accounting entry?

Professional studies (AICPA) and our own Sequence data put the manual entry error rate at between 3% and 5% of entries, with peaks reaching 8% during closing periods. These errors include transposed amounts, account coding errors, duplicates and VAT or date errors.

Can AI eliminate all accounting errors?

No. AI significantly reduces mechanical and repetitive errors (amounts, account coding, duplicates), but accounting judgement remains beyond its reach. Provisions, cut-off, unusual transactions and the overall consistency of the accounts require human expertise. In our data, the remaining error rate with AI is approximately 0,8%, a significant share of which relates to judgement.

How long does it take for an accounting AI agent to reach optimal accuracy?

Among our clients, AccountIA achieves a correct suggestion rate above 90% from the first month of use for recurring suppliers. After three months, this rate exceeds 94%. The agent learns continuously through human corrections, meaning that accuracy improves with time and the volume of data processed.

Does reducing accounting errors with AI work for Swiss SMEs?

Yes. The data presented in this article comes exclusively from Swiss SMEs and fiduciary firms (cantons of Vaud and Geneva). AccountIA is designed for the Swiss context: the SME chart of accounts, Swiss VAT rates (8,1%, 2,6%, 3,8%), Swissdec standards and the specific regulatory requirements of the Federal Tax Administration. The results are therefore directly applicable to SMEs in French-speaking Switzerland.

Last updated: 10 July 2026