Categories
- ACTUARIAL DATA SCIENCE
- AFIR / ERM / RISK
- ASTIN / NON-LIFE
- BANKING / FINANCE
- DIVERSITY & INCLUSION
- EDUCATION
- HEALTH
- IACA / CONSULTING
- LIFE
- PENSIONS
- PROFESSIONALISM
- Thought Leadership
- MISC
In this talk we will discuss how to design efficient algorithms in search of optimal premium rules in realistic insurance settings requiring reinforcement learning combined with function approximation. We will illustrate the appropriateness of the approximate optimal premium rule compared with the true optimal premium rule in a simplified setting and further demonstrate that the approximate optimal premium rule outperforms benchmark rules in more realistic insurance settings where classical approaches fail.
0 Comments
There are no comments yet. Add a comment.