Insurance, Risk Management, Data Science, Insurtech, Machine Learning.
be applied. Phase II: In this phase we train the Generator, and freeze the Discriminator. This is done by optimizing the Generator to create realistic samples that would fool the the trained Discriminator from Phase I. Once trained, the GANs model is optimized to produce synthetic time series