金融数据生成新方法，确保合规且零违规。
Constrained Tabular Diffusion for Finance
- 将可行性约束嵌入扩散采样过程，无需额外训练。
- 在大规模金融数据上实现零违规，提升稀缺数据可用性。
- 适合需严格合规的金融仿真与监管场景。
金融领域生成模型面临双重挑战：生成真实数据的同时满足严格的监管与经济目标，而标准表格扩散模型无法胜任。为此，我们提出面向金融的约束型表格扩散（CTDF），将采样阶段的可行性操作与混合类型表格扩散相结合。通过在逆向扩散采样循环中引入无训练的可行性算子，CTDF可强制执行硬性约束，适用于模拟、法律合规及外推等场景。在大规模金融数据集上的大量实验表明，该方法实现零约束违规，并显著提升稀疏数据的实用性。CTDF为生成可信、合规的合成数据提供了稳健方案，推动金融领域严谨生成建模与分析的发展。
原文摘要 · Abstract (English)
Generative models in finance face the dual challenge of producing realistic data while satisfying strict regulatory and economic objectives, a requirement that standard tabular diffusion models cannot provide. To address this difficulty, we introduce Constrained Tabular Diffusion for Finance (CTDF), a novel integration of sampling-time feasibility operations with mixed-type tabular diffusion in financial applications. By incorporating a training-free feasibility operator into the reverse-diffusion sampling loop, CTDF enforces hard constraints for applications such as simulation, legal compliance, and extrapolation. Extensive experiments on large-scale financial datasets demonstrate zero constraint violations and improvement in scarce data utility. CTDF establishes a robust method for generating trustworthy and compliant synthetic data, opening new avenues for rigorous generative modeling and analysis in the financial domain.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。