Actuarial Dataset Anonymization with anonym.plus

Strip direct identifiers from an actuarial dataset before analysts model it.

Actuarial anonymization is the removal of direct identifiers from a modelling dataset. GDPR Recital 26 says truly anonymous data falls outside the rules. anonym.plus marks each identifier on your device, so the figures stay analyzable while the people behind them are shielded.

When this applies

A modelling table mixes premiums and losses with the names behind each row. You strip those identifiers before the data feeds an analyst's model.

How anonym.plus handles it

  1. Open the dataset in anonym.plus on your device.
  2. The tool flags names, IDs, and contacts in each field.
  3. Local OCR reads any scanned source sheet.
  4. Turn the name map OFF for true anonymity.
  5. Swap or black out the confirmed identifiers.
  6. Save the clean table locally.

What you need to provide

PII & financial identifiers detected

Categoryanonym.plus entity typeExample
NamesPERSONpolicyholder name → [SUBJECT]
IdentifiersNATIONAL_IDnational ID → [ID]
FinancialMONEYclaim sum €41,200 → [AMOUNT]
ContactEMAIL_ADDRESSholder@example.com → [EMAIL]
DatesDATE_TIMEDOB 1984 → [DOB]
LocationLOCATIONpostcode → [REGION]

Compliance achieved

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Limitations & cautions

Recital 26 treats data as anonymous only if no one can re-identify a person. Rare combinations of age, region, and loss can still single someone out. Check for such outliers before you publish.

Frequently asked questions

When is a dataset truly anonymous under GDPR?

Recital 26 sets the bar at no reasonable means of re-identification. Remove direct identifiers and turn the name map off, then test for rare row combinations.

Why turn the name map off?

A name map can re-link a row to a person. For true anonymity under Recital 26, leave it off so no reverse path remains.

Is the source table uploaded?

No. The app runs locally, so the data never leaves your device.