Advanced Non-Life Pricing & Profitability: Machine Learning Techniques with R
Non-Life insurance is facing many challenges ranging from fierce competition on the market or evolution in the distribution channel 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: is our pricing adequate to cover the underlying cost of risk of my policyholders and the other costs we are facing? Which are the key variables driving the risk? Are they adequately taken into account in our pricing? What’s the impact of the claims history of my policyholder on its expected risk? In which segment are we profitable and in which are we not profitable?
• Competition: at what price will we attract the segments that we target and price out those that we do not want? Is the positioning of our competitors influencing our pricing practice and our profitability? What’s my position with respect to my competitors in term of pricing? What are the segments in which I am well positioned and the segments where I am not well positioned?
• Elasticity: what price (evolution) are our existing customers prepared to accept? Does the sensitivity to price evolution depend on the profile of my customer?
• Segmentation: is our segmentation granular enough for our purposes?
The aim of this seminar is to present some advanced actuarial/statistical techniques used in non-life pricing, competition analysis and profitability analysis. The seminar focuses on some practical problems faced by pricing actuaries and product managers and presents some new techniques used in non-life pricing in order to open new perspectives for product development (competition analysis, profitability analysis,…).
Organised by the EAA - European Actuarial Academy GmbH in cooperation with the Hellenic Actuarial Society.