GI Data Science Event London - The Complete 3 Day Training

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Actuartech

Actuarial Data Science Training: General Insurance (Pricing and Profitability)

167 City Rd

Sales are now closed. If you are still interested in attending this event, please contact us at info@actuartech.com to discuss options.Actuarial Data Science Training: General Insurance (Pricing and Profitability)3 day Training Event LondonWe 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. <a href="https://www.eventbrite.co.uk/e/88574156687">Day 1: 08:30-17:30 – Basics of non-Life pricing: introduction to GLMs in R</a><a href="https://www.eventbrite.co.uk/e/88574602019">Day 2: 09:00 - 17:30 – Advanced Methods of non-life pricing with R </a><a href="https://www.eventbrite.co.uk/e/88575003219">Day 3:. 09:00 - 17:30 – Other practical applications of Machine Learning in non-life insurance.</a> 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<a href="https://www.eventbrite.co.uk/e/88574156687">REGISTER FOR DAY 1 ONLY HERE</a>(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 counts10h45-12h30 − Case study: Developing a new technicaltariff for frequency13h30-15h30 − Gamma regression for attritional claims− Extreme value theory for large claimsmodelling− Case study: Developing a new technicaltariff for cost15h45-17h30 − Case study: Final technical tariff− Other practical difficulties with GLMDay 2: Advanced Methods of non-life pricing with R <a href="https://www.eventbrite.co.uk/e/88574602019">REGISTER FOR DAY 2 ONLY HERE</a>9h-10h30 − Modelling continuous explanatoryvariables with Generalized additivemodels: methodology and examples− Penalized regression techniques (Lasso,Ridge, interaction detection,…):methodology and examples10h45-12h30 − Introduction to supervised machinelearning algorithms, regression trees &amp;random forest− Example: Fitting a regression tree andrandom forest on frequency13h30-15h30 − Case Study: Regression tree and randomforest model adjustment for cost− Gradient Boosting Model (GBM)− Example: Fitting GBM on frequency− Case Study: GBM adjustment for cost15h45-17h30 − Artificial Neural Networks (ANN)− Example: Fitting ANN on frequency− Case Study: ANN adjustment for costDay 3: – Other practical applications of Machine Learning in non-life insurance. <a href="https://www.eventbrite.co.uk/e/88575003219">REGISTER FOR DAY 3 ONLY HERE</a>9h-10h30 − Data Management: Selection, PreAnalysis, Feature Engineering andFeature Selection− Case Study: Data analysis and featuresselection with random forest 10h45-12h30 − Case Study: Continuous Variablescategorization using regression trees orclustering methods− Case Study: Application of GBM methodto highlight interactions13h30-15h30 − Profitability and Competition analysis:profitability and positioning assessment,reverse engineering of competitorsprices15h45-17h30 − Introduction to unsupervised machinelearning algorithms (k-means and HAC):methodology and examples− Case study: ProfitabilityRequired materialAttendees 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.LanguageThe training will be conducted in English.

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