# Enhancing Actuarial Non-Life Pricing Models via Transformers

arXiv: https://arxiv.org/abs/2311.07597 · HTML: https://mlsanity.com/paper/2311.07597/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [On Embeddings for Numerical Features in Tabular Deep Learning](https://mlsanity.com/paper/2203.05556/) (2022)
- [Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning](https://mlsanity.com/paper/2103.03635/) (2021)
- [LocalGLMnet: interpretable deep learning for tabular data](https://mlsanity.com/paper/2107.11059/) (2021)
- [Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff](https://mlsanity.com/paper/2310.12671/) (2023)
- [A multi-task network approach for calculating discrimination-free insurance prices](https://mlsanity.com/paper/2207.02799/) (2022)

## Built on by (most cited first, 6 of 6)

- [TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models](https://mlsanity.com/paper/2511.08667/) (2025)
- [The Credibility Transformer](https://mlsanity.com/paper/2409.16653/) (2024)
- [In-Context Learning Enhanced Credibility Transformer](https://mlsanity.com/paper/2509.08122/) (2025)
- [Tab-TRM: Tiny Recursive Model for Insurance Pricing on Tabular Data](https://mlsanity.com/paper/2601.07675/) (2026)
- [Tree-like Pairwise Interaction Networks](https://mlsanity.com/paper/2508.15678/) (2025)
- [TabPFN Through The Looking Glass: An interpretability study of TabPFN and its internal representations](https://mlsanity.com/paper/2601.08181/) (2026)

## Abstract (arXiv)

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.