为银行生成金融交易数据，评估五种生成模型优劣。
Generative AI for Banks: Benchmarks and Algorithms for Synthetic Financial Transaction Data
- 对比五种生成模型在数据真实度、隐私保护等方面表现。
- CTGAN综合表现最佳，适合一般场景；DGAN最适配高隐私需求。
- 研究结果可指导银行选型，提升数据安全与模型训练效率。
银行业因数据敏感性和监管限制难以应用深度学习，生成式AI可能提供解决方案。本研究评估了五种领先模型——条件表格式生成对抗网络（CTGAN）、DoppelGANger（DGAN）、Wasserstein GAN、金融扩散模型（FinDiff）和表格式变分自编码器（TVAE）——在五个维度上的表现：数据保真度、合成质量、效率、隐私保护和图结构保留。结果显示，各模型均无法完全复现真实数据的图结构，但各有专长：DGAN在隐私敏感任务中表现最优，FinDiff与TVAE在数据复制与增强方面突出，而CTGAN在五项指标间取得平衡，适合中等隐私要求的通用场景。研究为金融机构选择合适算法提供了实证依据。
原文摘要 · Abstract (English)
The banking sector faces challenges in using deep learning due to data sensitivity and regulatory constraints, but generative AI may offer a solution. Thus, this study identifies effective algorithms for generating synthetic financial transaction data and evaluates five leading models - Conditional Tabular Generative Adversarial Networks (CTGAN), DoppelGANger (DGAN), Wasserstein GAN, Financial Diffusion (FinDiff), and Tabular Variational AutoEncoders (TVAE) - across five criteria: fidelity, synthesis quality, efficiency, privacy, and graph structure. While none of the algorithms is able to replicate the real data's graph structure, each excels in specific areas: DGAN is ideal for privacy-sensitive tasks, FinDiff and TVAE excel in data replication and augmentation, and CTGAN achieves a balance across all five criteria, making it suitable for general applications with moderate privacy concerns. As a result, our findings offer valuable insights for choosing the most suitable algorithm.
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