Benjamin Avanzi, Greg Taylor, Melantha Wang, Bernard Wong
arXiv 30 Jan 2023 · Statistics — Machine Learning · publishedAstin Bulletin (2024) · 17 citations (OpenAlex)
arXiv:2301.12710 · PDF · DOI · OpenAlex · Extracted main text
High-cardinality categorical features are pervasive in actuarial data (e.g. occupation in commercial property insurance). Standard categorical encoding methods like one-hot encoding are inadequate in these settings. In this work, we present a novel _Generalised Linear Mixed Model Neural Network_ ("GLMMNet") approach to the modelling of high-cardinality categorical features. The GLMMNet integrates a generalised linear mixed model in a deep learning framework, offering the predictive power of neural networks and the transparency of random effects estimates, the latter of which cannot be obtained from the entity embedding models. Further, its flexibility to deal with any distribution in the exponential dispersion (ED) family makes it widely applicable to many actuarial contexts and beyond. We illustrate and compare the GLMMNet against existing approaches in a range of simulation experiments as well as in a real-life insurance case study. Notably, we find that the GLMMNet often outperforms or at least performs comparably with an entity embedded neural network, while providing the additional benefit of transparency, which is particularly valuable in practical applications. Importantly, while our model was motivated by actuarial applications, it can have wider applicability. The GLMMNet would suit any applications that involve high-cardinality categorical variables and where the response cannot be sufficiently modelled by a Gaussian distribution.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Simchoni, G., Rosset, S (2022) Integrating random effects in deep neural networks | 1.000 | 7 | 3 | 100% |
| 2 | Sigrist, F (2022) Latent gaussian model boosting | 0.928 | 4 | 4 | 100% |
| 3 | Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D (2015) Weight uncertainty in neural networks | 0.737 | 3 | 2 | 100% |
| 4 | Gelman, A., Hill, J (2007) Data analysis using regression and multilevel/hierarchical models | 0.644 | 4 | 1 | 100% |
| 5 | Al-Mudafer, M.T., Avanzi, B., Taylor, G., Wong, B (2022) Stochastic loss reserving with mixture density neural networks self | 0.644 | 2 | 2 | 100% |
| 6 | Antonio, K., Beirlant, J (2007) Actuarial statistics with generalized linear mixed models | 0.644 | 2 | 2 | 100% |
| 7 | Denuit, M., Hainaut, D., Trufin, J (2019) Effective Statistical Learning Methods for Actuaries I: GLMs and Extensions | 0.644 | 2 | 2 | 100% |
| 8 | Kingma, D.P., Ba, J (2014) Adam: A method for stochastic optimization | 0.644 | 2 | 2 | 100% |
| 9 | Richman, R (2021) AI in actuarial science a review of recent advances part 2 | 0.644 | 2 | 2 | 100% |
| 10 | Richman, R (2021) AI in actuarial science a review of recent advances part 1 | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 52 scored citations.