Enhancing Actuarial Non-Life Pricing Models via Transformers
| Autor: | Brauer, Alexej |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1007/s13385-024-00388-2 |
| Popis: | 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. Comment: This preprint manuscript was uploaded to arXiv on November 10, 2023. It has not undergone peer review or any post-submission improvements or corrections. The peer reviewed Version of Record of this article is published in the European Actuarial Journal, and is available online at https://doi.org/10.1007/s13385-024-00388-2 |
| Databáze: | arXiv |
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