GI Data Science Event London - The Complete 3 Day Training

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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.