Explore building a technical tariff through GLMs in R to learn the basics of non-life pricing using data science techniques through Notebooks.
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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:
As Well As
Our Industry and Actuartech Resource Libraries which feature curated additional content to assist you on your data science journey.
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:
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.
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.
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.
