About This Event
In the insurance industry, the demand for granular data to drive ratemaking often clashes with increasingly strict privacy regulations. Traditional anonymization techniques struggle to find a balance, frequently stripping data of the utility required for accurate modeling. As a result, data teams are often forced to choose between rigorous compliance and the preservation of crucial analytic signals.
This webinar explores how advanced synthetic data generation is addressing this challenge. We will demonstrate methodologies that go beyond simple data masking to create high-fidelity datasets that retain the statistical properties of the original asset. By leveraging these techniques, privacy-safe derivatives can be generated which allow for robust model training and testing. We will look at strategies for synthesizing both flat files and multi-table relational data and how to maintain structural integrity in addition to fidelity.
