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：目前，在非寿险定价的神经网络领域已有大量研究。通常的目标是在当前行业标准广义线性模型的基础上，通过神经网络提升预测能力。本文通过创新方法，利用面向表格数据的Transformer模型增强非寿险精算模型，为这一前沿探索作出贡献。我们在组合精算神经网络与LocalGLMnet奠定的基础上，通过特征标记化Transformer对这些模型进行增强。本论文在真实世界索赔频率数据集上展示了所提方法的性能，并与广义线性模型、前馈神经网络、组合精算神经网络、LocalGLMnet及纯特征标记化Transformer等多种基准模型进行了比较。研究表明，新方法在保持广义线性模型部分优势的同时，能够取得优于基准模型的结果。本文还探讨了Transformer模型在精算场景中应用的实际意义与挑战。