Data Science Applications in Actuarial Work
What the exam tests
This domain tests whether you can place data science techniques in an actuarial business context and recognise where regulation, model risk and operational constraints change the answer.
Core applications
- Pricing and underwriting: risk classification, telematics, usage-based insurance and quote conversion.
- Claims: triage, fraud detection, subrogation and reserve estimation.
- Customer: retention, lapse prediction and next-best-action.
- Capital and ALM: scenario generation, proxy modelling and economic capital.
- Mortality and morbidity: experience analysis, longevity and health claims.
Rules
- A model must satisfy the use case: a pricing model needs explainability and regulatory acceptance as well as accuracy.
- Model risk management requires owners, documentation, independent validation, monitoring and a fallback.
- Consider feedback loops: a model that prices out a segment changes the data used to train the next model.
- Check legal constraints early: FCA Consumer Duty and price-walking rules, GDPR, and anti-discrimination law.
Worked example
A motor insurer uses telematics to price. The model predicts claim frequency with a 12% Gini improvement over the existing GLM. Before deployment, the team tests whether braking-hardness proxies for age or disability, reviews the FCA requirement to demonstrate fair value, and checks that the data processing is proportionate. The model is deployed only after adding an explainability note, a monitoring dashboard and a challenge process. The improvement alone is not sufficient.
Traps
- Presenting accuracy metrics without a business baseline.
- Ignoring the cost of false positives and false negatives in fraud detection.
- Assuming a model validated at launch remains valid; drift is normal.
- Overlooking that a variable prohibited in underwriting may still be used indirectly.