EAA-webinar - Generative AI for Synthetic Actuarial Data

A substantial amount of data in the insurance industry is confidential and proprietary, making it challenging to validate new methodologies. Even when data owners are willing to share anonymised datasets, the effort required to mask the data and navigate bureaucratic processes can often prevent disclosure. This environment limits opportunities to measure progress in the field.

To address the scarcity of real insurance data, there is an opportunity to develop simulated datasets that capture features similar to those observed in practice, thereby reducing friction in data disclosure for development and research. Although synthetic data generation is an active area within the broader machine learning community, its specific application to insurance data remains underexplored.

This web session is designed to demonstrate how to build synthetic insurance datasets using various generative models.