  Synthetic data generation has emerged as a crucial topic for financial
institutions, driven by multiple factors, such as privacy protection and data
augmentation. Many algorithms have been proposed for synthetic data generation
but reaching the consensus on which method we should use for the specific data
sets and use cases remains challenging. Moreover, the majority of existing
approaches are ``unsupervised'' in the sense that they do not take into account
the downstream task. To address these issues, this work presents a novel
synthetic data generation framework. The framework integrates a supervised
component tailored to the specific downstream task and employs a meta-learning
approach to learn the optimal mixture distribution of existing synthetic
distributions.
