MSTabVAE: Multi-Step Latent Conditional Variational Autoencoder for Imbalanced Tabular Data Synthesis

Recent advances in artificial intelligence have expanded applications in the financial domain, particularly in fraud detection, a critical task for preventing losses for both customers and institutions. However, fraud detection is challenging due to severe class imbalance, which significantly degrades detection performance. Existing synthetic data generation methods for minority-class augmentation often fail to capture the heterogeneous structure and complex feature relationships of tabular data in the financial domain. To address these challenges, we propose MSTabVAE, a novel generative framework that extends TabNet, a deep learning architecture for tabular data, into a conditional variational autoencoder (CVAE) framework. MSTabVAE preserves TabNet’s step-wise feature-selection mechanism and introduces a multi-step latent mapping strategy to capture complex feature relationships in heterogeneous tabular data. Class-conditional information is further injected at every decision step to target minority-class synthesis. Experiments on four imbalanced financial datasets with two downstream classifiers show that augmenting training data with MSTabVAE-generated samples consistently improves classification performance over existing generative and traditional oversampling baselines, achieving up to a 42% relative improvement in Recall@1%FPR on the highly imbalanced BAF dataset. Additional experiments verify the fidelity of the generated data through inter-feature correlation and marginal distribution preservation, negligible privacy leakage as measured by the Distance to Closest Record metric, and up to 238× speedup in generation time over diffusion-based baselines. Finally, MSTabVAE provides structural interpretability for the generation process by revealing the features selected at each decision step.