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.

翻译：当前，神经网络在非寿险定价领域的研究成果丰硕。通常目标是在广义线性模型（现行行业标准）基础上，通过神经网络提升预测能力。本文通过创新方法，利用适用于表格数据的Transformer模型增强精算非寿险模型，为此领域做出贡献。我们基于组合精算神经网络与LocalGLMnet奠定的基础，通过特征分词器Transformer对这些模型进行增强。本文基于真实索赔频率数据集展示了所提方法的性能，并将其与广义线性模型、前馈神经网络、组合精算神经网络、LocalGLMnet及纯特征分词器Transformer等基准模型进行对比。研究表明，新方法在保持广义线性模型部分优势的同时，能取得优于基准模型的结果。本文还讨论了将Transformer模型应用于精算领域的实际影响与挑战。