France Value Added Tax Number
Detects France Value Added Tax Number patterns. This pattern is based on a Microsoft Purview built-in sensitive information type. VAT numbers have country-specific prefixes that aid detection accuracy.
- Type
- regex
- Engine
- universal
- Confidence
- high
- Confidence justification
- High confidence: the FR prefix followed by a 2-character validation key and 9 digits is a distinctive format for French VAT numbers. Added context gating and exclusion rules improve precision and reduce incidental matches.
- Detection quality
- Verified
- Jurisdictions
- fr, eu
- Regulations
- GDPR
- Frameworks
- ISO 27001, ISO 27701
- Data categories
- financial, business-identifier
- Scope
- narrow
- Risk rating
- 5
- Platform compatibility
- Purview: Compatible, GCP DLP: Compatible, Macie: Compatible, Zscaler: Compatible, Palo Alto: Compatible, Netskope: Compatible
Pattern
\bFR[A-Z0-9]{2}\d{9}\bCorroborative evidence keywords
TVA, taxe sur la valeur ajoutée, numéro de TVA, VAT, intracommunautaire, [object Object], value added tax, VAT number, belasting, [object Object], Mehrwertsteuer, [object Object], Umsatzsteuer, VAT registration, BTW-nummer, Steuernummer, tax number, tax registration, adószám, imposta sul valore aggiunto (+52 more)
Proximity: 300 characters
Should match
FRAB123456789— French VAT with letter validation keyFR12345678901— French VAT with numeric validation keyFRX1987654321— French VAT number format
Should not match
FR1234567890— Only 10 digits after FR prefix, too shortDE123456789AB— German prefix with trailing letters, not French VATtemplate example placeholder record identifier— Template/sample context should be excluded even when anchor words are present
Known false positives
- Other identifier schemes that coincidentally share a similar prefix and digit structure Mitigation: Validate the complete format including prefix and digit count. Layer with document context to confirm financial or tax-related content.
- Test or example VAT numbers used in documentation or training materials Mitigation: Maintain an allow-list of known test/example numbers. Use document classification to distinguish production data from training content.