Non-Life Pricing Using Machine Learning Techniques with R Applications
Announcement from the EAA organiser:
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 web session is to present some advanced actuarial/statistical techniques used in non-life pricing, competition analysis and profitability analysis. The web session 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,…).
The web session is developed for non-life actuaries or statisticians but also for managers working in product development or risk management departments. Participants should ideally have basic knowledge of non-life pricing.
Attendees are encouraged to have a laptop computer with R installed as well as some useful packages (all the information will be provided after subscription). A basic knowledge of the R software is useful.
The web session will alternate between methodological concepts, practical examples and case studies in order to ensure a comprehensive understanding of the techniques presented.
The participants will be requested to look at 4 e-learning modules (of around 30 minutes each) presenting the basics of machine learning before the web session. The access to these e-learning modules will be granted up to the end of the web session. These 4 e-learning modules are:
- Introduction to Machine learning
- Supervised learning (parts 1 and 2)
- Unsupervised learning
The case studies will be performed by the participants with the R software. An individual support will be available during the case study sessions thanks to breakout rooms.
Technical Requirements
Please check with your IT department if your firewall and computer settings support web session participation (the programme Zoom will be used for this online training). Please also make sure that you are joining the web session with a stable internet connection.
Click here to make a reservation: Your early-bird registration fee is € 650.00 plus 19% VAT for bookings by 6 March 2023. After this date, the fee will be € 845.00 plus 19% VAT.
Julie Zians
Julie holds a BSc. Mathematics from the University of Liège (ULg) and a MSc. Actuarial Sciences from the University of Louvain (UCL). She is a qualified actuary of the Institute of Actuaries in Belgium (IA|BE). Julie is a certified Programmer in SAS from the SAS institute and further programs regularly in R or Visual Basic. After a six months internship at Reacfin in 2011, she joined the firm in 2012. She is a member of the Non-Life Center of Excellence and has performed several projects in Non-Life Insurance (pricing and capital modelling) but also in Health Insurance.
Michaël Lecuivre
Michaël holds a Bachelor and Master in Physics from the Catholic University of Louvain (UCL) and a Master in Actuarial Sciences, also from the UCL. He is a qualified actuary of the Institute of Actuaries in Belgium (IA|BE) and also the winner of the IA|BE best master thesis in 2016.
During his time as a consultant Michaël has worked on multiple Non-life missions such as Non-life technical pricing, profitability analysis, competition analysis, BSCR computations and aggregations under Solvency II, reporting optimization and finally risk management. All this allowed him to gain a good expertise in SAS, R and Python as well as a good knowledge of statistical models (GLM, GAM,GLMM …) and machine learning algorithms (regression trees, random forest, GBM …). Michaël is also a trainer at the “Data Science Certificate” organized by IA|BE in Belgium.
Xavier Maréchal
Xavier is founder and CEO of Reacfin. Xavier is one of the co-authors of “Actuarial Modeling of Claim Counts: Risk Classification, Credibility and Bonus-Malus Systems” (Wiley, 2007). Xavier has obtained different academic degrees as Master in Engineering (Applied Mathematics), MSc. Actuarial Sciences and MSc. Management. Xavier is a qualified actuary of the Institute of Actuaries in Belgium (IA|BE). Xavier has extensive experience in the actuarial field obtained during his 18 years as a principal consultant for many national and multinational insurance companies. He has gained a complementary experience in various fields going from Non-Life ratemaking and provisioning to health modeling and ALM. After several years of intensive modeling activities in health, non-life and ALM, Xavier works now as reviewer and mentor for consultants. He performed several validation assignments and holds the actuarial function for a health insurance company.