AI in JPK and VAT Control: How to Detect Anomalies Before Filing a Return

Where AI Catches Errors Earlier Than People Do

In an accountant's work, the greatest value lies not in simple "automatic checking" but in catching discrepancies before the JPK file is submitted. According to the Academy of Business material, AI can support the analysis of JPK_CIT through sample selection, anomaly detection and identification of discrepancies, and in the tax area such tasks shorten the path from data to correction.

According to amavat, automation can organise documents, read data, match payments, flag anomalies and speed up reporting, but it should not independently decide on the VAT rate, the country of taxation, the obligation to register for VAT, the recording of transactions in OSS or the local return. This is an important boundary: AI is meant to find a warning signal, not to replace the tax assessment.

How the Control Mechanism Should Work

The most effective approach is to compare several data layers at once. LeewayHertz describes an approach in which an anomaly is detected by comparing account balances, account movements, forecasts, transaction volumes and known business events, as well as by analysing consistency between documents.

In practice, for JPK and VAT such a mechanism can work in three steps:

1. Comparing JPK items with source data from invoices, the bank and the sales system.
2. Detecting deviations from a pattern, such as an unusual amount, a duplicate, a missing counterparty or an inconsistent settlement period.
3. Flagging items for manual verification before the return is submitted.

According to LeewayHertz, anomaly models are also well suited to detecting inconsistent entity names, transaction descriptions, amounts and reporting periods across files. This is exactly the type of control that reduces the risk of a formally correct entry reaching the return while being substantively inconsistent.

Which Warning Signals Are Most Valuable

The most practical rules are those that catch deviations from the company's own history, not only general "errors". The material on the tax application of AI shows that systems analyse patterns across returns, e-invoices, registrations and taxpayer behaviour over time. From the LeewayHertz perspective, anomalies may include unexpected zero-rated VAT transactions, a sudden change in rate or inconsistent treatment of similar products.

In JPK and VAT control, it is therefore worth checking above all:

1. Duplicate invoices and entries.
2. Mismatches in amounts between the sales register, the purchase register and payments.
3. Unusual VAT rates for recurring transactions.
4. Missing counterparty data or settlement period data.
5. Transactions deviating from the company's historical profile.

How to Implement This Without a False Sense of Security

AI works best when it has clean, standardised data and clearly defined deviation thresholds. Deftername emphasises the importance of data cleansing, setting anomaly thresholds, continuous monitoring and integration with banking systems and e-documents. Without this, the model will flag too many false alarms or miss significant deviations.

For a finance team, the practical principle is simple: first automatic detection of the deviation, then quick verification by the accountant, and only at the end the submission of the JPK file or VAT return. This arrangement reduces the number of corrections and organises control before submission, instead of moving it to the stage of reacting after an error.

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