In this seminar, we are going to focus on some topics in actuarial science including life and non-life insurance. We are specifically dealing with how data analytics is used to design a better insurance contract. Further, we tackle different types of risk inherent in a life insurance contract and optimal retirement products. The seminar is based on scientific papers that summarize recent results in this area.

The seminar is suitable for Master students in Mathematik, Wirtschaftsmathematik, Wirtschaftswissenschaften or Finance. Previous knowledge in Life, Health, and Pension Mathematics, Insurance Economics and Financial Mathematics 1 are helpful.

Typically, seminar papers are distributed to a group of 2 students.
The seminar performance consists of three parts:

•  A seminar presentation about a selected topic. The presentation typically includes some
  theoretical derivations / model introduction and some numerical part that applies the
  esults in a realistic setup.
  Duration of the presentation: 90 minutes (including discussion).
• A written formulation of the presentation documents as a support for the participants of
  a maximum length of two pages.
  Delivery of the presentation documents: at least one week before the presentation via email
  to peter.hieber@uni-ulm.de. The presentation documents are created jointly.
• Active participation in this seminar.

Based on the performance, every participant will be credited with an (internal) grade.

1. Chen, A., Rach, M., & Sehner, T. (2020). On the optimal combination of annuities and
   tontines. ASTIN Bulletin, 50(1), 95-129.
2. Chen, A., Hieber, P. & Klein, J. (2019). Tonuity: a novel individual-oriented
   retirement plan. ASTIN Bulletin, 49(1), 5-30.
3. Denuit, M. (2020): Investing in your own and peers’ risks: the simple analytics of P2P
   insurance, European Actuarial Journal 10(2).
4. Eckert, J. and Gatzert, N. (2018): Risk- and Value-Based Management for Non-Life
   Insurers under Solvency Constraints, European Journal of Operational Research Vol.
   266, No. 2, pp. 761-774.
5. Nadine Gatzert, A. Martin, M. Schmidt, B. Seith, N. Vogl (2020): Portfolio
   Optimization with Irreversible Long-Term Investments in Renewable Energy under
   Policy Risk: A Mixed-Integer Multistage Stochastic Model and a Moving-Horizon
   Approach, in: European Journal of Operational Research forthcoming.
6. Milevsky, M. A., & Salisbury, T. S. (2015). Optimal retirement income tontines.
   Insurance: Mathematics and Economics, 64, 91-105.
7. Wüthrich, M. V. (2018). Machine learning in individual claims reserving.
   Scandinavian Actuarial Journal, 2018(6), 465-480.
8. Wüthrich, M. V. (2018). Neural networks applied to chain–ladder reserving.
   European Actuarial Journal, 8(2), 407-436.