A Data Augmentation Method for Financial Datasets Using Diffusion Models with Self-Attention Mechanism

In recent years, financial institutions have faced challenges in risk assessment and intelligent decision-making due to data scarcity and privacy concerns. To address this issue, we propose a novel data augmentation method based on Diffusion Models enhanced with a self-attention mechanism for financial datasets. Our approach introduces self-attention into the reverse denoising process of diffusion models to effectively capture the global dependencies among financial variables, thereby significantly improving the quality of synthesized financial data. Experimental results demonstrate that the proposed diffusion model outperforms traditional generative adversarial networks (GANs) in terms of fidelity and distribution alignment, achieving higher data quality and consistency. Additionally, comprehensive ablation studies validate the effectiveness of integrating selfattention mechanisms into diffusion models. This research highlights the potential of diffusion-based data augmentation in addressing data scarcity and privacy issues in the financial domain.