Investigating Climate Risk in R (R8)

In this case study, we create a climate risk index to investigate climate change using time series techniques.

Introduction

If you’re looking to learn how to utilise R for non-traditional use cases in non-life insurance, 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
  • 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 a coding project 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 serve as an end-to-end walkthrough of an investigation into climate change analysis through applying time series machine learning techniques, specifically ARIMA and SARIMA modelling. 

The aim of using machine learning techniques within this context is to better understand the climate data, the key drivers behind climate change, how various time series models can be fitted to forecast future climate-related risks and draw conclusions on its potential impact on the insurance industry by building a climate risk index. We will guide you through importing, cleaning, investigating, model fitting, visualising, and interpreting your data.

This course is presented through our training platform and uses Jupyter Notebooks, with the code and explanations embedded, to facilitate interactive coding. This allows you to run the code and make your own tweaks to see how it affects the output. We encourage you to explore the techniques presented here outside of the course as well by, for example, running the code using a different dataset or by tackling a slightly different problem statement.

This end-to-end case study aims to assist users in answering what is climate change and the risk associated with it and how life as well as general insurers can address the problem of climate change and steps to be taken to mitigate these risks into the workings of an insurance-pricing industry. We will demonstrate, with the use of a practical case study, how the full cycle of actuarial analysis is evolving - from data collection and data enhancement, to preliminary analysis, modelling, verification and, ultimately, application and communication.

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Investigating Climate Risk in R

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Course Structure

Chapter 1 introduces the case study and offers an overview of the business context and climate risk modelling approaches. 

Chapter 2 discusses the data, data preparation, and packages used in this case study, such as tseries, caret, and tidyverse.

In Chapter 3 we reload the data to perform a preliminary analysis where after we plot some graphs to visually analyse the data.

Chapter 4 provides an overview of time series and performs time series modelling on various climate components in R.

In Chapter 5 we build and manage a climate risk index and use this index in an insurance actuarial-pricing example. 

Chapter 6 concludes the course by providing a summary of the results and how climate change may impact financial statements.

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 and teams with a good grasp of the fundamentals of R.
  • Individuals and teams wanting to learn how to use time series modelling in R.
  • Individuals and teams wanting to learn how to build a risk index.
  • Individuals and teams working in pricing and reserving.

Why is this topic important?

  • Climate change is a topical issue impacting various spheres of life and business.
  • Climate change poses additional risks to people and property and should therefore be included in actuarial work.
  • R has powerful time series tools which assist in accurate and efficient forecasting.

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

Investigating Climate Risk in R

Sign up for a free preview of this R case study

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Preview

£150 once-off (3-month access)

Enroll Today

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