Den Cramérska assuransmatematiken
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Name and title: Leo LeveniusPhD Student
ORCID0000-0002-0433-883X Länk till annan webbplats.
Workplace: Department of Mathematics (incl. Math. Statistics) Länk till annan webbplats.
Visiting address Room B1361Albano hus 1
Office hours Contact me for a meeting.
Postal address Matematiska institutionen106 91 Stockholm
PhD Student in Survival Analysis (Mathematical Statistics) since October 2025. Supervised by Associate Professor Mathias Lindholm.
I hold an MSc in Actuarial Mathematics (2025) and two BScs in Mathematical Statistics (2023) and Mathematics (2024), all from Stockholm University, where I have been a student since 2020. I was previously an Amanuensis at the Department of Mathematics from 2023 to 2025.
Last updated: February 10, 2026
Spring 2026: I will be a supervisor for students writing their Bachelor's Theses in Mathematical Statistics (MT6001).
I have previously worked on the courses Probability Theory I, Probability Theory II, Introduction to Data Processing, Statistical Data Processing, Introductory Course in Mathematics, Challenging Mathematics, and Risk Models and Valuation in Life and Disability Insurance.
My research focuses on how we can use different types of machine learning, primarily boosting and large language models, to model and predict life and health processes.
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No Abstract Available
This paper aims to introduce the reader to the premium setting of annuities within life insurance. This is done using a hypothetical annuity contract offered to 36-year-olds in Sweden. The contract provides an annual pension from age 65 until either the individual's death or age 90, after which payouts cease. The analysis employs life tables using real-life data to estimate mortality, discounting to decide present values, and calculates fair and risk-adjusted premia for lump sum and annual payment options using theory and simulations. Ultimately, we found that the method used was insufficient given the data. This is due to the last decades' rise in life expectancy, requiring us to use other methods to acquire accurate premia.
No Abstract Available