Enhancing Actuarial Non-Life Pricing Models via Transformers

TL;DR

This paper introduces transformer-based models to improve non-life insurance pricing, outperforming traditional and neural network benchmarks while maintaining GLM benefits, demonstrated on real-world claim data.

Contribution

It presents novel transformer-based methods for actuarial non-life models, enhancing existing neural network approaches with feature tokenization for better predictive performance.

Findings

Transformer models outperform benchmarks in claim frequency prediction.

Proposed methods retain GLM advantages in interpretability.

Real-world dataset demonstrates practical effectiveness.

Abstract

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.