Data Anonymisation in Practice for Insurance Intermediaries: A Brief Guide.

What Is Anonymisation?

Anonymisation transforms personal data so individuals cannot be identified by anyone, using any reasonably likely method. Anonymised data is outside the scope of UK GDPR.

Why It Matters: It enables safe data sharing, analytics, and innovation without breaching privacy laws. This can be particularly useful for insurance intermediaries looking to share data with insurers, create management information dashboards, or train AI tools while respecting client privacy.

✅ When to Use Anonymisation in Insurance Intermediaries

Sharing aggregated claims data with insurers: Removes the need for individual data sharing agreements if the data is truly anonymised.
Creating Management Information (MI) dashboards (e.g., for business performance analysis): Aggregation and masking reduce re-identification risks when visualising trends.
Training AI tools (e.g., for customer service or risk assessment): Proper anonymisation is essential to avoid privacy risks when using client data to train AI models.

Responding to Freedom of Information Act (FOIA), Environmental Information Regulations (EIR), or Re-use of Public Sector Information Regulations (RPSI) requests: Anonymise personal data within the requested information before disclosure or re-use, if applicable.

Transparency and Accountability: Providing anonymous information can increase transparency and accountability to individuals.
Research Purposes: Sharing anonymous data for research can enable wider societal benefits.

❌ When Anonymisation is Likely Not Appropriate:

Sending marketing emails: This inherently requires personal data to identify recipients.
Outsourcing administrative tasks (e.g., claims processing) where data can still be linked to clients: In such cases, anonymisation has not been effectively applied.
Profiling live clients for decision-making purposes: This requires identifiable data.
Using location, dates, or account-level data in small populations increases the risk of re-identification.
Sharing data with outsourced suppliers where re-identification risks exist: Third-party transfer and processing of identifiable data require adherence to GDPR.
Feeding datasets into third-party AI tools that may store or learn from identifiable information: Ensure proper anonymisation before using such services.

🚩 Common Misconceptions

"Removing names is enough"

❌ Reality: Data linked to policy numbers, postcodes, or unique combinations of data points is still personal data. Consider the risk of re-identification using other available information.

"If I can’t identify the client, no one can"

❌ Reality: Consider external datasets or third-party knowledge that could enable re-identification. The "motivated intruder" test requires you to consider what a reasonably determined person with access to other information might be able to do.

"Pseudonymised data is anonymous"

❌ Reality: Pseudonymised data remains subject to UK GDPR rules as it can still be attributed to a specific individual through additional information. Anonymisation is a distinct process from pseudonymisation. Pseudonymisation is a risk reduction technique, but does not render data anonymous.

🔐 Good Anonymisation Practices for Insurance Intermediaries

To ensure anonymisation is effective:

✅ Make it irreversible in practice. The risk of identifying people should be reduced to a sufficiently remote level.

✅ Tailor methods to the context. Consider the specific dataset, the purpose of anonymisation, and the potential for re-identification, especially given the size and nature of your client base. Smaller client bases may carry a higher re-identification risk.

✅ Regularly review techniques as technology evolves. New data analysis and linking methods may emerge, potentially undermining previous anonymisation efforts. Review your identifiability risk assessments regularly.

✅ Use proportionate measures based on risk levels. The anonymisation techniques should be appropriate for the data's sensitivity and intended use.

⚙️ Workflow for Insurance Intermediaries

1. Is it personal data? If the data relates to an identifiable individual, it is personal data.

2. If yes → consider if anonymisation suits your purpose. Anonymisation is a privacy-friendly way to harness the potential of data.

3. Assess re-identification risks: Evaluate your client base size, the uniqueness of the data fields you are considering anonymising, and the potential availability of external datasets that could be used to link the data. Consider the "spectrum of identifiability".

4. Define the purpose: For what specific reason are you anonymising the data (e.g., AI training, MI reporting, external sharing)? The purpose will influence the rigor of the anonymisation techniques required. Use stricter methods and test the anonymisation effectiveness if the data will be used for sensitive purposes or shared externally.

5. Document your reasoning: Keep records explaining how and why data was classified as anonymous, including the anonymisation techniques and re-identification risk assessment.

6. Review regularly: Re-identification risks can evolve. Revisit your anonymisation processes and risk assessments every 6–12 months, or sooner if there are significant changes to the data or available technologies.

Techniques You Can Use

Aggregation: Summarise data at a group level (e.g., total number of policies by region) to avoid identifying individuals.
Masking: Remove directly identifiable fields such as names, addresses, and contact details.
Data Swapping/Noise Injection: Slightly alter numerical values (e.g., date of birth by a few days) or swap values between records within acceptable ranges to obscure individual patterns.
Suppression: Remove or redact small categories or outliers within the data that could potentially reveal identities, especially in smaller datasets.

🛑 When Anonymisation Is NOT Enough

Avoid relying solely on anonymisation when:

❌ Profiling live clients for decision-making purposes.

❌ Using granular location, date, or account-level data in small client populations.

❌ Sharing data with outsourced suppliers where there's a risk of re-identification during processing.

❌ Feeding datasets into third-party AI tools that might retain or learn identifiable patterns.

📣 When in doubt, treat the data as personal. It is better to apply data protection safeguards than to mistakenly assume data is anonymous.

🧭 Support for Insurance Intermediaries

This desk aid is based on guidance from the Information Commissioner's Office (ICO). For more detailed information, refer to the full ICO guidance on anonymisation and the UK GDPR. The ICO website contains comprehensive resources and advice on data protection. Consider seeking tailored advice if you have specific or complex anonymisation requirements.

By following this guide, UK insurance intermediaries can approach data anonymisation with greater confidence, ensure compliance with data protection regulations, and leverage the benefits of data analysis and sharing.