Synthetic Data for Insurance ML: When It Helps and When It Lies
Synthetic data — artificially generated records that mimic the statistical shape of real data — is having a moment in insurance, and for understandable reasons. It promises to solve the two constraints every insurance data team runs into: privacy restrictions on using real customer data, and not having enough examples of rare events. It genuinely helps with some of that. It also quietly lies in ways that matter. Knowing which is which saves you from a model that performs beautifully in testing and fails on the cases you built it for. Where synthetic data genuinely earns its place 1. Development and testing environments. The clearest win, and the least controversial. Your developers need realistic data to build against; giving them a copy of production means real customer records on laptops. Synthetic data with the same shape, distributions, and edge cases lets people build safely. If you do nothing else with synthetic data, do this. 2. Sharing beyond the boundary. Working wit...