In case you would like to organize this masterclass within your company feel free to contact us at learning@reacfinacademy.com.
Digital training
Non-Life insurance is facing many challenges ranging from fierce competition on the market and evolution in the distribution channels used by the consumers, to evolution of the regulatory environment.
Pricing is the central link between solvency, profitability and market shares (volume). Improving pricing practice encompasses several dimensions:
- Technical
- Competition
- Customer behaviour
- Segmentation
The aim of this masterclass is to:
1. present basic and more advanced actuarial/statistical techniques used in non-life pricing, competition analysis and profitability analysis,
2. focus on some practical problems faced by pricing actuaries and product managers by presenting practical examples and running case studies with the participants,
3. introduce machine learning techniques used in non-life pricing to open new perspectives for product development (competition analysis, profitability analysis…).
Non-Life insurance is facing many challenges ranging from fierce competition on the market and evolution in the distribution channels used by the consumers, to evolution of the regulatory environment.
Pricing is the central link between solvency, profitability and market shares (volume). Improving pricing practice encompasses several dimensions:
- Technical
- Competition
- Customer behaviour
- Segmentation
The aim of this masterclass is to:
1. present basic and more advanced actuarial/statistical techniques used in non-life pricing, competition analysis and profitability analysis,
2. focus on some practical problems faced by pricing actuaries and product managers by presenting practical examples and running case studies with the participants,
3. introduce machine learning techniques used in non-life pricing to open new perspectives for product development (competition analysis, profitability analysis…).
Become one of the 140+ participants, who have reported excellent feedback since the first edition in 2019:
[...] In general, I think you explain very clear even quite dense concepts and have a good balance between rough theory (which is necessary to understand) and a proper visualization of it. Thanks a lot for it !
Thank you very much for this open and transparent knowledge-share! Not only theoretical basis, but also very practical examples and with sharing of solution to be able to build something on your own. This is a very interesting training!
It was a good course to learn more about machine learning.
For the last public session, they granted the training courses an average of:
Overall content (4,8/5)
E-learning content level (4,8/5)
Slides used in the webinars (4,8/5)