In this case study, we create a climate risk index to investigate climate change using time series techniques.
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:
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.
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.
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.
We have tailored packages available to ensure that corporate teams have the option to attend structured live lessons by our tutors, and the option to request a practical assignment and bespoke feedback. Invoicing option available.
