Tickets
Description
This event has now ended. For further information regarding previous or upcoming events, please email info@actuartech.com
Actuarial Data Science Training: General Insurance (Pricing and Profitability)
3 day Training Event London
We are pleased to present a 3 day hands on general insurance training event in London.
The aim of this workshop is to:
- Present basic and more advanced actuarial and statistical techniques used in non-life pricing, competition analysis and profitability analysis.
- Explore practical problems faced by pricing actuaries and product managers by working through examples and case studies
- Introduce machine learning techniques used in non-life pricing in order to open new perspectives for product development (for example competition analysis and profitability analysis).
Participation is flexible: you can sign up for Day 1, Day 2, Day 3 or all days according to your needs and availability. Please see below a brief outline of each training day with a link to the relevant event and additional course information.
Day 1: 08:30-17:30 – Basics of non-Life pricing: introduction to GLMs in R
Day 2: 09:00 - 17:30 – Advanced Methods of non-life pricing with R
Day 3:. 09:00 - 17:30 – Other practical applications of Machine Learning in non-life insurance.
The event is hosted by Actuartech and Reacfin.
The event will count towards CPD.
Day 1: Basics of non-Life pricing: introduction to GLMs in R
(This session could be useful for life actuaries looking for an introduction to GLM)
08:30 - Registration
9h-10h30 − Introduction to risk classification
− From linear to generalized linear models
− Poisson regression for claim counts
10h45-12h30 − Case study: Developing a new technical
tariff for frequency
13h30-15h30 − Gamma regression for attritional claims
− Extreme value theory for large claims
modelling
− Case study: Developing a new technical
tariff for cost
15h45-17h30 − Case study: Final technical tariff
− Other practical difficulties with GLM
Day 2: Advanced Methods of non-life pricing with R
9h-10h30 − Modelling continuous explanatory
variables with Generalized additive
models: methodology and examples
− Penalized regression techniques (Lasso,
Ridge, interaction detection,…):
methodology and examples
10h45-12h30 − Introduction to supervised machine
learning algorithms, regression trees &
random forest
− Example: Fitting a regression tree and
random forest on frequency
13h30-15h30 − Case Study: Regression tree and random
forest model adjustment for cost
− Gradient Boosting Model (GBM)
− Example: Fitting GBM on frequency
− Case Study: GBM adjustment for cost
15h45-17h30 − Artificial Neural Networks (ANN)
− Example: Fitting ANN on frequency
− Case Study: ANN adjustment for cost
Day 3: – Other practical applications of Machine Learning in non-life insurance.
9h-10h30 − Data Management: Selection, PreAnalysis, Feature Engineering and
Feature Selection
− Case Study: Data analysis and features
selection with random forest
10h45-12h30 − Case Study: Continuous Variables
categorization using regression trees or
clustering methods
− Case Study: Application of GBM method
to highlight interactions
13h30-15h30 − Profitability and Competition analysis:
profitability and positioning assessment,
reverse engineering of competitors
prices
15h45-17h30 − Introduction to unsupervised machine
learning algorithms (k-means and HAC):
methodology and examples
− Case study: Profitability
Required material
Attendees are encouraged to bring 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 will be useful, but not required.
Language
The training will be conducted in English.
