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IAI Actuarial Core Principles · Actuarial Statistics · Purpose and function of data analysis

An Indian health insurer wants to analyse claims data containing policyholder name, Aadhaar number, age, pin code and claim amount, and share it with an external research team. Which step best balances usefulness of the analysis with data protection duties?

The best approach is to remove direct identifiers like name and Aadhaar number and generalise quasi-identifiers such as age and pin code into bands. This reduces re-identification risk while keeping the data useful for analysis, unlike initials or deleting key fields.

  1. AShare the full file, because the researchers are professionals
  2. BDelete the claim amount field, since it is the most sensitive
  3. CRemove direct identifiers such as name and Aadhaar number, and group age and pin code into broad bandsCorrect
  4. DReplace each name with its initials but keep all other fields unchanged
  5. Share only the records of policyholders who have never made a claim

Explanation

Direct identifiers should be removed and quasi-identifiers like exact age and pin code generalised to reduce re-identification risk while retaining analytic value. Initials still leave identifiable data. Deleting claim amounts destroys the purpose of the analysis, and sharing only non-claimants gives a biased dataset.

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