Insurance Pricing Analyst Interview Questions UK: 8 Real Questions and What Interviewers Want

Before your Insurance Pricing Analyst interview, it helps to see the questions from the interviewer's side. We have picked eight from our set of 16 and added the reason each one gets asked.

Eight Insurance Pricing Analyst interview questions, and what the interviewer is thinking

1. Talk me through how you would build a generalised linear model to price a motor or home insurance book.

Why they ask: GLMs are the foundation of UK personal lines pricing, so this is almost always asked.

2. What does a pricing analyst do day to day, and why does this work appeal to you?

Why they ask: They want to see that you understand the pricing cycle and have a genuine interest in the work.

3. Which pricing tools and languages have you used, such as Emblem, Radar, Python or SQL, and what did you do with them?

Why they ask: They need to know you can be productive in their technical environment.

4. How do you check whether a pricing model is performing well and avoid overfitting?

Why they ask: A model that looks good in-sample but fails live is costly, so this separates strong analysts.

5. How have the FCA's rules on renewal pricing changed the way you set new business and renewal prices?

Why they ask: Pricing analysts must understand how regulation constrains their models in practice.

6. What are the advantages and risks of using gradient boosting machines instead of GLMs for pricing?

Why they ask: The industry is moving towards machine learning, but regulators expect explainability.

7. Tell me about a time your analysis changed a pricing or underwriting decision.

Why they ask: They are looking for evidence that your analysis creates business impact, not just reports.

8. The loss ratio on one segment has deteriorated sharply over the last three months. What steps would you take?

Why they ask: This tests your analytical process and ability to separate noise from genuine change.

A sample answer to question 1

Talk me through how you would build a generalised linear model to price a motor or home insurance book.

I would model claim frequency and average severity separately, or pure premium with a Tweedie model. Frequency usually uses a Poisson or negative binomial distribution with a log link and exposure as an offset, and severity uses a gamma distribution with a log link, with large claims capped and handled separately. Before modelling, I would clean the data, check exposure and claims reconcile to finance, and develop recent periods to ultimate. I would then explore one-way and two-way relationships, group levels sensibly, for example banding vehicle age, and add factors one at a time, checking statistical significance, consistency over time and logical sense. I would test for interactions, such as age by vehicle group, and validate on a holdout set using lift charts and the Gini coefficient. Finally I would smooth the factors, combine the frequency and severity models into relativities, and consider business constraints before moving into rate optimisation.

Common mistake to avoid: Treating software output as the answer; skipping data reconciliation

Want the rest?

Full model answers, key points and common mistakes for all 16 questions are on the Insurance Pricing Analyst interview questions page. The first five are free, no sign-up needed.