Enhancing Actuarial Non-Life Pricing Models via Transformers
Alexej Brauer
Currently, there is a lot of research in the field of neural networks for
non-life insurance pricing. The usual goal is to improve the predictive power
via neural networks while building upon the generalized linear model, which is
the current industry standard. Our paper contributes to this current journey
via novel methods to enhance actuarial non-life models with transformer models
for tabular data. We build here upon the foundation laid out by the combined
actuarial neural network as well as the localGLMnet and enhance those models
via the feature tokenizer transformer. The manuscript demonstrates the
performance of the proposed methods on a real-world claim frequency dataset and
compares them with several benchmark models such as generalized linear models,
feed-forward neural networks, combined actuarial neural networks, LocalGLMnet,
and pure feature tokenizer transformer. The paper shows that the new methods
can achieve better results than the benchmark models while preserving certain
generalized linear model advantages. The paper also discusses the practical
implications and challenges of applying transformer models in actuarial
settings.