aiqbench analysis
This paper benchmarks synthetic data generation techniques, specifically comparing Multivariate Imputation by Chained Equations (MICE) against deep learning models like Variational Autoencoders and Conditional GANs, for actuarial ratemaking applications. The study evaluates how well these methods preserve marginal distributions and multivariate relationships to improve Generalized Linear Models when original data is scarce or privacy-constrained. Results suggest that MICE-based approaches offer high-fidelity tabular data with lower implementation complexity than their deep learning counterparts in this specific domain.
Synthetic data for ratemaking: imputation-based methods vs adversarial networks and autoencoders
arXiv:2509.02171v3 Announce Type: replace-cross Abstract: Actuarial ratemaking depends on high-quality data, yet access to such data is often limited by the cost of obtaining new data, privacy concerns, etc. In this paper, we explore synthetic-data generation as a potential solution to these issues. In addition to generative methods previously studied in the actuarial literature, we explore and benchmark another class of approaches based on Multivariate Imputation by Chained Equations (MICE). In a comparative study using an open-source dataset, MICE-based models are evaluated against other generative models like Variational Autoencoders and Conditional Tabular Generative Adversarial Networks. We assess how well synthetic data preserves the original marginal distributions of variables as well as the multivariate relationships among covariates. The consistency between Generalized Linear Models (GLMs) trained on synthetic data with GLMs trained on the original data is also investigated. Furthermore, we assess the ease of use of each generative approach and study the impact of generically augmenting original data with synthetic data on the estimation of GLMs for predicting claim counts. Our results highlight the potential of MICE-based methods in creating high-fidelity tabular data while offering lower implementation complexity compared to deep generative models.
Abstract:Actuarial ratemaking depends on high-quality data, yet access to such data is often limited by the cost of obtaining new data, privacy concerns, etc. In this paper, we explore synthetic-data generation as a potential solution to these issues. In addition to generative methods previously studied in the actuarial literature, we explore and benchmark another class of approaches based on Multivariate Imputation by Chained Equations (MICE). In a comparative study using an open-source dataset, MICE-based models are evaluated against other generative models like Variational Autoencoders and Conditional Tabular Generative Adversarial Networks. We assess how well synthetic data preserves the original marginal distributions of variables as well as the multivariate relationships among covariates. The consistency between Generalized Linear Models (GLMs) trained on synthetic data with GLMs trained on the original data is also investigated. Furthermore, we assess the ease of use of each generative approach and study the impact of generically augmenting original data with synthetic data on the estimation of GLMs for predicting claim counts. Our results highlight the potential of MICE-based methods in creating high-fidelity tabular data while offering lower implementation complexity compared to deep generative models.
Machine Learning (stat.ML); Machine Learning (cs.LG); Applications (stat.AP)
arXiv:2509.02171 [stat.ML]
(or arXiv:2509.02171v3 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2509.02171 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 284 words
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