Enhancing Actuarial Non-Life Pricing Models via Transformers
This work addresses the need for more accurate pricing models in the actuarial insurance domain, though it is incremental as it builds upon prior neural network methods.
The paper tackled the problem of improving predictive power in non-life insurance pricing by enhancing existing actuarial models with transformer architectures for tabular data, achieving better results than benchmark models like generalized linear models and neural networks while preserving some GLM advantages.
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.