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Data Science in Insurance

Insurance companies bring security to society by offering protection against financial losses. Through the pooling of similar risks, insurers allow to trade uncertainty for certainty by transferring the risk from individuals facing the loss to the insurer, in exchange for a premium. Non-life (e.g. motor, fire, liability), life and health insurers work in a data driven business, and are constantly confronted with the challenges created by rapidly increasing technical and computer facilities for data collection, storage and analysis. Predictive modeling (or: insurance analytics) is a cornerstone of the insurance industry.  

This course explains the working principles of insurance and the many tasks within insurance where predictive modeling is relevant.  

Find full course description in the course catalogue.    


The grade for the course consists of two parts:

  • Coursework counting app. 40 %
  • 3-hour written exam counting app. 60 %

The written exam is online and presence in Aarhus is not nescessary.

Admission Requirements

Course specific:

A Bachelor's degree in Economics and Business Administration or a related degree.


Exchange students: nomination from your home university

Freemovers: documentation for English Langauge proficiency

You can read more about admission here


Katrien Antonio


Academic profile


Katrien Antonio is Associate Professor at KU Leuven in Belgium. Her main academic interests are Insurance analytics, Actuarial science and Quantitative risk measurement.