Non-Life Pricing Using Machine Learning Techniques with R Applications

Einleitung/Dauer

Non-Life insurance is facing many challenges ranging from fierce competition in the market or evolution in the distribution channel used by 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 terms 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 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,…).

Preliminary Programme
Tuesday, 8 December 2026
09:00-10:30 Data Selection, Pre-Analysis and Feature Selection (data quality, pre-treatment, missing values, feature engineering and feature selection)
Overfitting and cross-validation 
Example: Data Analysis and filtering
10:30-10:45 Break
10:45-12:30 Regression tree models
Example: Fitting a regression tree on frequency
12:30-12:45 Break
12:45-14:00 Bagging and random forest models
Example: Fitting a random forest on frequency
Case Study: Random forest model adjustment for cost

Wednesday, 9 December 2026
09:00-10:30 GBM models
Case Study: GBM model adjustment for frequency
10:30-10:45 Break
10:45-12:30 Explainable machine learning techniques (PDP, H statistics, Shape value, EBM)
12:30-12:45 Break
12:45-13:45 Case study: Features selection, partial dependence plot and Shapley Value

Thursday, 10 December 2026
09:00-10:30 Case study: Application of GBM method to highlight interactions
Unsupervised machine learning 
10:30-10:45 Break
10:45-12:30 Profitability analysis: profitability and positioning assessment with ML techniques
Example: profitability analysis with regression trees 
12:30-12:45 Break
12:45-13:45 Competition analysis: understanding competitors prices
Example: Reverse engineering of competitors prices
Client behaviour and Elasticity 

All the above times are given in CET (Central European Time).

Vorgehensweise und Ziele

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.

Teilnehmer

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. A basic knowledge of the R software is useful.

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 to join the web session with a stable internet connection.

Attendees are encouraged to have a laptop computer with R installed as well as some useful packages (all the information will be provided).

Dozierende

Xavier Maréchal (Reacfin)
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.

Michaël Lecuivre (Reacfin)
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 also the winner of the IABE (Institute of Actuaries in Belgium) best master thesis of 2016. He is currently the Lead Pricing in Reacfin Non-Life Center of Excellence. 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 and R as well as a good knowledge of statistical models (GLM, GAM,GLMM …) and machine learning algorithms (regression trees, random forest, GBM …). He is involved in various missions as modelling, implementation and validation of pillar I deliverables (standard approach and (Partial) Internal models), reinsurance optimization, model documentation, non-life pricing model development for several lines of business, etc.. Michaël is a qualified actuary of the Institute of Actuaries in Belgium (IA|BE).

Julie Zians (Reacfin)
Julie is a Senior Manager at Reacfin. 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.

Sprache/Kurztitel

The language of the web session will be English.

CPD Credits
For this web session, the following CPD credits are available under the CPD scheme of the relevant national actuarial association:

• Austria: 13 points
• Belgium: 13 points
• Bulgaria: 15 points
• Croatia: individual accreditation
• Czech Republic: 13 hours
• Denmark: 13 credits
• Estonia: 13 hours
• Finland: 8.5 points
• France: 75 points
• Germany: 13 hours
• Greece: 17 points
• Hungary: 13 hours
• Iceland: 13 credits
• Ireland: 13 hours
• Italy: approx. 4 credits (individual accreditation)
• Latvia: 13 hours
• Lithuania: 13 hours
• Netherlands: approx. 13 PE-points (individual accreditation)
• Norway: 13 points
• Poland: 13 hours
• Portugal: 13 hours
• Russia: 40 points
• Serbia: 5 hours
• Slovakia: 8 points
• Slovenia: 50 points
• Spain: CAC: 13 hours, IAE: 13 hours
• Switzerland: 15 points
• USA: SOA (Section B): up to 15.60 hours

No responsibility is taken for the accuracy of this information.