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This tutorial demonstrates workflows to incorporate text data into actuarialclassification and regression tasks. The main focus is on methods employingtransformer-based models. A dataset of car accident descriptions with anaverage length of 400 words, available in English and German, and a datasetwith short property insurance claims descriptions are used to demonstrate thesetechniques. The case studies tackle challenges related to a multi-lingualsetting and long input sequences. They also show ways to interpret modeloutput, to assess and improve model performance, by fine-tuning the models tothe domain of application or to a specific prediction task. Finally, thetutorial provides practical approaches to handle classification tasks insituations with no or only few labeled data, including but not limited toChatGPT. The results achieved by using the language-understanding skills ofoff-the-shelf natural language processing (NLP) models with only minimalpre-processing and fine-tuning clearly demonstrate the power of transferlearning for practical applications.

Actuarial Applications of Natural Language Processing Using Transformers: Case Studies for Using Text Features in an Actuarial Context