EmDT: Embedding Diffusion Transformer for Tabular Data Generation in Fraud Detection

ArXi:2603.13566v1 Announce Type: cross Imbalanced datasets pose a difficulty in fraud detection, as classifiers are often biased toward the majority class and perform poorly on rare fraudulent transactions. Synthetic data generation is therefore commonly used to mitigate this problem. In this work, we propose the Clustered Embedding Diffusion-Transformer (EmDT), a diffusion model designed to generate fraudulent samples.