Synthetic data for ratemaking: imputation-based methods vs adversarial networks and autoencoders

ArXi:2509.02171v2 Announce Type: replace-cross 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