Enhancing Actuarial Non-Life Pricing Models via Transformers

ArXiv ID: 2311.07597 “View on arXiv”

Authors: Unknown

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.

Keywords: Non-life insurance pricing, Transformer models, Feature tokenizer transformer, Claim frequency prediction, Generalized linear model (GLM)

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper presents mathematically sophisticated transformer architectures for tabular data and combines them with established actuarial models, yet it validates these methods on a single real-world dataset with standard benchmarks, demonstrating practical backtest-readiness.

  flowchart TD
    A["Research Goal<br>Enhance Actuarial Pricing with Transformers"] --> B["Data<br>Real-world Claim Frequency Dataset"]
    B --> C["Methodology<br>Feature Tokenizer Transformer Models"]
    C --> D{"Benchmarks"}
    D --> E["GLM, FNN, CAN, LocalGLMnet, FTT"]
    D --> C
    C --> F["Computational Process<br>Train & Evaluate Hybrid Models"]
    F --> G["Key Findings<br>Superior Performance & GLM Advantages"]