Actuarial Data Science Tutorials

On this page we present all the tutorials that have been prepared by the working party. We are intensively working on additional ones and we aim to have approx. 10 tutorials, covering a wide range of Data Science topics relevant for actuaries.

All tutorials consist of an article and the corresponding code. In the article, we describe the methodology and the statistical model. By providing you with the code you can easily replicate the analysis performed and test it on your own data.

Case Study 9: Convolutional neural network studies: (1) anomalies in mortality rates (2) image recognition

Article on SSRN

Code on GitHub

Case Study 8: Peeking into the Black Box: An Actuarial Case Study for Interpretable Machine Learning

Article on SSRN

R Code on GitHub ; R Markdown on GitHub

Case Study 7: The Art of Natural Language Processing: Classical, Modern and Contemporary Approaches to Text Document Classification

Article on SSRN

Code on GitHub

Case Study 6: Lee and Carter go Machine Learning: Recurrent Neural Networks

Article on SSRN

Code on GitHub

Case Study 5: Unsupervised Learning: What is a Sports Car?

Article on SSRN

Code on GitHub ; R Markdwon on GitHub

Case Study 4: On Boosting: Theory and Applications

Article on SSRN

Code on GitHub

Case Study 3: Nesting Classical Actuarial Models into Neural Networks

Article on SSRN

Code on GitHub

Case Study 2: Insights from Inside Neural Networks

Article on SSRN

Code on GitHub

Case Study 1: French Motor Third-Party Liability Claims

Article on SSRN

Code on GitHub, consisting of standard R, R using TensorFlow and R using H2O.