Insurance Pricing

The goal of insurance pricing is to charge each customer a premium that reflects the risk they bring to the insurer. If the premium is too low, the insurer loses money on that policy. If it is too high, the customer goes to a competitor. Getting this balance right requires good data, robust models, and an understanding of the market.

The daily work involves analysing historical claims data, identifying the risk factors that drive claim frequency and severity, and building models that translate those factors into a price. You also monitor existing pricing models over time to check whether they remain accurate as the customer mix or claims environment changes. Presenting pricing recommendations to commercial and actuarial teams is a regular part of the role.

The main methods are generalised linear models, which are the industry standard for insurance pricing, and increasingly machine learning techniques for more complex risk segmentation. The main tools are R and Python, with actuarial software like Emblem used at some insurers. Knowledge of claims data, statistical modelling, and the insurance products being priced is important.

The work sits within the Actuarial Sector and connects closely to the Actuary and Data Scientist roles. Pricing work is done by actuaries, data scientists, and pricing analysts depending on the organisation. In the Netherlands, insurers are supervised by DNB and must be able to justify their pricing models to regulators.