  This tutorial demonstrates workflows to incorporate text data into actuarial
classification and regression tasks. The main focus is on methods employing
transformer-based models. A dataset of car accident descriptions with an
average length of 400 words, available in English and German, and a dataset
with short property insurance claims descriptions are used to demonstrate these
techniques. The case studies tackle challenges related to a multi-lingual
setting and long input sequences. They also show ways to interpret model
output, to assess and improve model performance, by fine-tuning the models to
the domain of application or to a specific prediction task. Finally, the
tutorial provides practical approaches to handle classification tasks in
situations with no or only few labeled data, including but not limited to
ChatGPT. The results achieved by using the language-understanding skills of
off-the-shelf natural language processing (NLP) models with only minimal
pre-processing and fine-tuning clearly demonstrate the power of transfer
learning for practical applications.
