Actuarial Data Science

Lecturer

Dr. Stefan Schelling

Exercises

Leonard Gerick

Dates

Monday, 2:15 to 3:45 PM [N24-H14]

Tuesday, 10:15 to 11:45 AM [N24, 226]

First lecture: Monday, 17.04

First exercise session (Introduction to R): Tuesday, 18.04

Important Information

All course material will be provided via moodle.
It is strongly recommended that participants register for the course on moodle before the beginning of the semester.

Type

2/2 SWS

See LSF for the module description (including ECTS).

Prerequisites

Module „Life-, Health- and Pension-Mathematics“ is recommended but not required

Intended audience

Master students in Mathematics, Master students in Business Mathematics (Wirtschaftsmathematik), Master students in Mathematical Biometry and Master students in Finance.

Content

The course consists of two parts:

Mortality models in life and pension insurance:
- For example:
  - Deterministic models
  - Stochastic models
  - Age-Period-Cohort model
  - Multifactor models with parameter uncertainty
  - etc.
Parametric and non-parametric approaches in non-life insurance pricing:
- For example
  - Modeling non-life insurance claims
  - Prediction Uncertainty
  - Cross-Validation methods
  - Generalized linear models
  - Neural networks
  - Data compression
  - Issue of over-parametrization and over-fitting
  - etc.

Literature

Details on the literature will be provided at the beginning of the semester.

Cairns, A. J., Blake, D., and Dowd, K. (2006). A two-factor model for stochastic mortality with parameter uncertainty: theory and calibration. Journal of Risk and Insurance, 73(4):687-718.
Denuit, M., Hainaut, D., and Trufin, J. (2019/2020). Effective Statistical Learning Methods for Actuaries I - III, Springer Actuarial Lecture Notes.
Lee, R. D. and Carter, L. R. (1992). Modeling and forecasting us mortality. Journal of the American statistical association, 87(419):659-671.
Wüthrich, M. V. and Buser, C. (2020). Data analytics for non-life insurance pricing. Swiss Finance Institute Research Paper