Please note: The algorithm descriptions in English have been automatically translated. Errors may have been introduced in this process. For the original descriptions, go to the Dutch version of the Algorithm Register.

Mass corrected income tax return (MC)

Within the Inland Revenue, we use Mass Correction (MC) for Income Tax returns. MC involves comparing returns with available third-party data. This involves declarations in which there are relatively simple corrections in the citizen's return.

Last change on 1st of May 2025, at 15:26 (CET) | Publication Standard 1.0
Publication category
Impactful algorithms
Impact assessment
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Status
In use

General information

Theme

Public finance

Begin date

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Contact information

algoritmeregister@belastingdienst.nl

Link to publication website

https://over-ons.belastingdienst.nl/onderwerpen/omgaan-met-gegevens/algoritmeregister/

Link to source registration

https://over-ons.belastingdienst.nl/onderwerpen/omgaan-met-gegevens/algoritmeregister/massaal-corrigeren-aangifte-inkomensheffing-ih/

Responsible use

Goal and impact

Within the Inland Revenue, we use Mass Correction (MC) for Income Tax returns. MC involves comparing returns with available third-party data. These are declarations that involve relatively simple corrections in the citizen's return. To determine the target group of citizens eligible for MC of their IH returns, taxpayers are selected on the basis of objective tax characteristics.

For part of the selected group of citizens, the income tax return is automatically adjusted and finalised. In these cases, there is a correction in the citizen's favour. For those citizens who disagree with the final assessment, the option of objection and appeal is open.

The other part of the selected group of citizens will receive advance notice of the correction on the income tax return. This group of citizens is given an opportunity to respond to the advance notice. The possibility of objection and appeal is also open to citizens in this case.

Considerations

MC is important for timely and careful processing of IH returns. The algorithm can support an Inland Revenue employee in doing so. As a result, the assessment is more careful, efficient and uniform.


By using the algorithm, the Tax Administration can perform its tasks more efficiently and effectively than in manual processing. It also increases the quality of the Tax Administration's tax return processing and contributes to the performance of its statutory task.

Human intervention

Human intervention in the Tax Administration context implies that a competent and knowledgeable employee plays a substantial role in decision-making.

In the operation of the algorithm MC, there is no human intervention. The algorithm makes the decisions.


Based on selection rules, the algorithm assesses whether the income tax return qualifies for correction and final determination. This happens only if the correction is in the taxpayer's favour.

In case of a correction to the disadvantage of the citizen, a preliminary notice is always sent. If the citizen responds, manual processing by an employee follows.

Risk management

The Inland Revenue is taking several measures to manage the risks of using the algorithm.

  • 1. Transparency

The Tax and Customs Administration publishes information about the algorithm in the Tax and Customs Administration's algorithm register and the Central Government's algorithm register.

This allows citizens to understand the use and operation of the algorithm.

  • 2. Automated decision-making

The description of an algorithm explains this and also details the aspect of human intervention.

  • 3. Privacy protection and legality

The Tax Authority ensures that the use of the data is assessed against the General Data Protection Regulation (GDPR).

The description of the algorithm also links to the applicable laws and regulations.

  • 4. Responsibility

On the basis of the Algorithm Register Policy Framework, the responsibilities and tasks in the use of algorithms are clearly described and secured.

  • 5. Monitoring and evaluation

The operation of the algorithms is periodically tested. Based on this, selection rules can be adjusted.

Legal basis

  • General State Tax Act
  • General Administrative Law Act
  • General Data Protection Regulation
  • General Data Protection Regulation (Implementation) Act
  • Payroll Tax Act 1964
  • Income Tax Act 2001
  • Citizen Service Number (General Provisions) Act
  • Archives Act 1995

Links to legal bases

  • Algemene wet inzake rijksbelastingen: https://wetten.overheid.nl/BWBR0002320/
  • Algemene wet bestuursrecht: https://wetten.overheid.nl/BWBR0005537/
  • Algemene verordening gegevensbescherming : https://eur-lex.europa.eu/legal-content/NL/TXT/HTML/?uri=CELEX:32016R0679
  • Uitvoeringswet algemene verordening gegevensbescherming : https://wetten.overheid.nl/BWBR0040940/
  • Wet op de Loonbelasting 1964: https://wetten.overheid.nl/BWBR0002471/
  • Wet Inkomstenbelasting 2001: https://wetten.overheid.nl/BWBR0011353/
  • Wet algemene bepalingen burgerservicenummer: https://wetten.overheid.nl/BWBR0022428/
  • Archiefwet 1995: https://wetten.overheid.nl/BWBR0007376/

Elaboration on impact assessments

The use of the data should be assessed against the AVG.


The AVG prescribes that no more data should be used than necessary. This is called data minimisation. The Inland Revenue regularly reviews whether the data used is still necessary and therefore may be used.

Operations

Data

  • Identifying data (including BSN)
  • Counter information (financial and life event data)
  • Company data
  • Data from previous income tax returns (financial data and personal data)

Links to data sources

  • Identificerende gegevens (o.m. BSN): Basisregistratie Personen (BRP)
  • Contra-informatie (financiële gegevens en gegevens over levensgebeurtenissen): Belastingdienst
  • Bedrijfsgegevens: Belastingdienst
  • Gegevens uit voorgaande aangiften inkomstenheffing (financiële gegevens en persoonlijke gegevens): Belastingdienst

Technical design

The algorithm consists of selection rules created by content experts based on laws, regulations and expertise.

The algorithm is not self-learning. This means that the algorithm does not develop itself during its use.


Based on selection rules, the algorithm assesses whether the income tax return qualifies for correction and final determination. This only happens if the correction is in the taxpayer's favour.

In case of a negative correction, an advance notice is always sent.

External provider

The algorithm was developed by staff at the Inland Revenue and is also maintained internally.

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