Signal model 'Common reporting standard / Foreign Account Tax Compliance Act (CRS/FATCA)

Dutch Tax and Customs Administration

CRS / FATCA is a selection tool used to select sizeable CRS/FATCA signals that meet criteria of the Concealed Assets programme.

Last change on 18th of June 2025, at 8:37 (CET) | Publication Standard 1.0

• Publication category: Impactful algorithms
• Impact assessment: Field not filled in.
• Status: In use

General information

Theme

Public finance

Begin date

2021

Contact information

[e-mail]

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/signaalmodel-common-reporting-standard-foreign-account-tax-compliance-act-crs-fatca/

Responsible use

Goal and impact

The signal model Signal model 'Common Reporting Standard / Foreign Account Tax Compliance Act (CRS/FATCA) is designed to select which income tax/ national insurance contributions(IH) returns can be processed from citizens holding assets abroad.

The CRS/FATCA signal model compares the data received from foreign cooperation partners with the data entered by the citizen in his IH return. If there is a discrepancy between the data received and the data entered by the citizen, the model can select the IH declaration for processing. The signalling model checks whether a citizen is required to declare his foreign assets in his declaration. This takes into account the Tax Administration's available capacity to process the returns.

Considerations

The signal model is important for achieving the objectives of the Tax Administration and the VhV programme, which we want to do carefully. The algorithm can support a Tax Administration employee to do this. As a result, the assessment is more careful, efficient and uniform. It involves tens of thousands of data per year.

The signal model contributes to the systematic and accurate checking of returns. By using the signal model, these declarations can be checked more quickly. Citizens get clarity faster as a result.

The signal model determines the assessment based on the relevant data. This makes the process less error-prone and more efficient.

Human intervention

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

Human intervention is always involved in the operation of the signal model. The signal model selects the return that can be handled. It is the Tax Administration employee who makes the decision.

Risk management

• Privacy and AVG

The use of data is tested against the General Data Protection Regulation (AVG). Reviewing personal data reveals any privacy risks and allows appropriate measures to be taken.

The AVG prescribes that no more data should be used than necessary. This is called data minimisation. The Tax Administration regularly examines whether the data used are still necessary and can therefore be used.

• Equality and non-discrimination

The selection rules in the algorithm are tested against non-discrimination legislation. Processing as little personal data as possible reduces the risk of direct discrimination. Employees involved in developing and managing the algorithms receive training on data protection and bias.

• Safeguards

The General Administrative Law Act (Awb) requires government actions to be transparent and lawful. The Tax and Customs Administration observes the general principles of good governance when applying and developing algorithms.

Conditions, a quality framework, have been drawn up by the Tax and Customs Administration for the development of algorithms. This contains rules and agreements that are followed during algorithm development. The conditions of the National Audit Authority are leading in this respect. At set moments, the Tax and Customs Administration checks whether the algorithm still meets the quality requirements.

The algorithm uses data collected under various tax laws. As required by the AVG, no more data is used than necessary.

The selection rules are reviewed periodically and adjusted if necessary to remain compliant with laws and regulations.

Legal basis

1. General State Tax Act:
2. General Administrative Law Act:
3. General Data Protection Regulation:
4. General Data Protection Regulation Implementation Act:
5. Payroll Tax Act 1964:
6. Income Tax Act 2001:
7. Corporation Tax Act 1969:
8. Turnover Tax Act 1968:
9. General Provisions Citizens' Service Number Act:
10. 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 op de Vennootschapsbelasting 1969: : https://wetten.overheid.nl/BWBR0002672/
• Wet op de Omzetbelasting 1968: : https://wetten.overheid.nl/BWBR0002629/
• Wet algemene bepalingen Burgerservicenummer:: https://wetten.overheid.nl/BWBR0022428/
• Archiefwet 1995: : https://wetten.overheid.nl/BWBR0007376/

Operations

Data

1. Personal data taxpayer
2. IH tax return data
3. CRS/FATCA data

Links to data sources

• Persoonsgegevens belastingplichtige: Basisregistratie Personen (BRP)
• Aangiftegegevens IH: Belastingdienst
• CRS/FATCA gegevens: Buitenlandse samenwerkingspartners

Technical design

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

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

Using the signal model selects declarations that are manually reviewed by a staff member.

External provider

The algorithm was developed by Tax Administration staff and is also maintained internally.