Catastrophe Claims Dataset Anonymization with anonym.plus

Strip direct identifiers from a catastrophe claims dataset before analysis.

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

When this applies

An event dataset ties each loss to a named policyholder and a location. You strip those identifiers before the data feeds a CAT model.

How anonym.plus handles it

  1. Open the dataset in anonym.plus on your device.
  2. The tool flags names, IDs, and contacts per row.
  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]
FinancialMONEYloss €38,500 → [AMOUNT]
LocationLOCATIONloss postcode → [REGION]
DatesDATE_TIMEevent date 2025 → [DATE]
ContactEMAIL_ADDRESSholder@example.com → [EMAIL]

Compliance achieved

Anonymize catastrophe claims datasets offline — see plans & start free →

Limitations & cautions

Recital 26 treats data as anonymous only if no one can re-identify a person. A precise postcode plus a large loss can still single someone out. Coarsen such fields before you publish.

Frequently asked questions

When is an event dataset truly anonymous?

Recital 26 sets the bar at no reasonable means of re-identification. Remove direct identifiers, then coarsen rare location and loss combinations.

Why coarsen the postcode?

A precise location can re-identify a household after a major event. Reduce it to a wider region to meet the Recital 26 standard.

Is the dataset uploaded?

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