Amputation-imputation based generation of synthetic tabular data for ratemaking
This work addresses data scarcity and privacy issues in insurance ratemaking, but it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of limited high-quality data in actuarial ratemaking by exploring synthetic-data generation, introducing MICE-based methods and comparing them to other generative models like VAEs and CTGANs, with results showing MICE's potential for creating high-quality tabular data while being more user-friendly.
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 discussing generative methods previously studied in the actuarial literature, we introduce to the insurance community another approach based on Multiple Imputation by Chained Equations (MICE). We present a comparative study using an open-source dataset and evaluating MICE-based models 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. We also investigate the consistency between Generalized Linear Models (GLMs) trained on synthetic data with GLMs trained on the original data. Furthermore, we assess the ease of use of each generative approach and study the impact of augmenting original data with synthetic data on the performance of GLMs for predicting claim counts. Our results highlight the potential of MICE-based methods in creating high-quality tabular data while being more user-friendly than the other methods.