Basics of Non-life Pricing in R (R5)

Explore building a technical tariff through GLMs in R to learn the basics of non-life pricing using data science techniques through interactive Jupyter Notebooks.

Introduction

If you’re looking to learn the fundamentals of pricing in R, this course is ideal for you. An individual subscription gives you 3 months’ online access to:

  • Course materials
  • A personal coding environment through Jupyter Notebooks
  • Discussion forums to engage and collaborate with like-minded individuals
  • Instructional videos
  • Option to ask tutors questions through forums and Q&A sessions
  • Hands-on practical examples linked to actuarial work
  • On demand access

As Well As

Our Data Science Resource Library which features Actuartech and Industry specific curated additional content to assist you on your data science journey.

You can also request to access to coding challenges to practice the skills you learn in this course.

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Pick from any of our introductory or advanced courses with bespoke insurance and actuarial specific case studies.

Our platform is easy to use and embeds the coding environment and learning material in one place to enable you to apply data science hands-on.

We provide case studies and projects relevant to actuarial work, and based on relevant datasets provided. You have the option to interact and network with your peers.

Overview

This course will allow you to explore utilising building a technical tariff with generalised linear models (GLMs) in R. 

The course starts with an introduction to risk classification, introducing portfolio heterogeneity, risk classification, technical vs commercial risk pricing, and the process. Thereafter it explains the need for regression and the move from linear regression to GLMs, introducing a variety of GLM families.

We then explore Poisson regression for claim counts as a frequency model, how to code categorical explanatory variables, how to interpret the likelihood equations, finding the variance and deviance, testing the hypothesis, and analysing the claim frequencies.

Lastly, we consider overfitting and how to assess the relevance of models, introducing the following as severity models:

  • Gamma regression for attritional claims 
  • Logistic regression for claims occurrence 
  • Extreme value theory for large claims modeling

The course ends with the live lesson which deals with the practical difficulties of GLMs. There is reference to two practical experiences, the example and the hands-on case study. The example showcases Poisson, Gamma and logistic regression in R, whereas the case study is a hands-on experience in developing a new technical tariff. Both Notebooks, along with a memo, are available for completion on the platform.

This course is presented mainly through a combination of videos, slides, and Jupyter Notebooks. After each video, there is a short quiz for the student to gauge their understanding of the section before continuing.

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Basics of Non-life pricing in R

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Course Structure

Chapter 1 introduces risk classification and the models this course discusses.

Chapter 2 offers practical examples of the models as a Jupyter Notebook.

Chapter 3 is a hands-on case study for developing a new technical tariff, also using Jupyter Notebooks.

Chapter 4 discusses the practical difficulties with GLMs.

The Appendix contains further resources to assist the student in their data science journey.

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Who's this course suitable for?

  • Individuals with some experience in and basic knowledge of R.
  • Individuals interested in learning how ML can assist with pricing and how it can offer advanced insights into data.
  • Individuals wanting to learn how to build GLMs from the ground up.
  • Individuals with an interest in risk classification.
  • Individuals working in pricing and reserving.

Why is this topic important?

  • GLMs are a common & powerful technique for non-life insurers.
  • We offer an open-source alternative for building various GLMs which offers greater flexibility in model building and can be integrated into various systems.
  • Risk classification is important for determining reserves and managing regulatory solvency margins.

Short note to say really enjoyed today’s webinar. It had a very clear message. […] fully in agreement with the comments that it is imperative we maintain our professional and ethical stance at all times if we want to continue to be trusted and relied on.

Webinars

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Short note to say really enjoyed today’s webinar. It had a very clear message. […] fully in agreement with the comments that it is imperative we maintain our professional and ethical stance at all times if we want to continue to be trusted and relied on.

Webinars

I just wanted to say what an interesting presentation that was. Thank you so much for taking the time to put this on for us, it is very much appreciated by all – especially the flexibility around hosting as a webinar instead of the original [in-person] format. It worked very well indeed!

Webinars

I think I’m [one of the first actuaries in my area] who are pointing towards Data Science, creating the new [role] of Actuarial data Scientist. For this reason i [sic] decided to follow a post graduate master in Business Intelligence and Big data analytics. I'm actively following your company and i [sic] think it is one of the best Actuarial consulting company [sic] who [sic] is pointing towards data Science!

Webinars

I love your videos - being free and accessible really helped me. The Q&A session was fantastic! It always comes down to execution and I feel this should always accompany your presentations - answering the question of how will your participants use what you give them. Keep up the great work!

Round Table

Thank you for having me along. I really found it the most motivating conversation I’ve had in a while, and made me think about what I’m trying to achieve within this area. We all need evenings like that to get some perspective on what we *think* is going on and what actually is. It was a very good evening.

Round Table

It was a really good introduction to Data Science and afterwards I felt that I now have a platform that I could use to further my understanding in this area.

Webinars

I am really happy to have been part of the talk. It was very insightful and please keep doing more of this. I am a data science student currently but I have an actuarial background. I worked in life insurance for about 5 months before resigning to do my masters in Data science so that I blend the actuarial world and Data science together. The talk gave me perspective. Even suggested some potential topics for my Thesis.

Webinars
Get started

Basics of Non-life pricing in R

Sign up for a free preview of this introductory course in R

Free Preview

Preview

£300 once-off (3-month access)

Enroll Today

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