标题： FinDiff：用于金融表格数据生成的扩散模型 显示英文标题

标题： FinDiff: Diffusion Models for Financial Tabular Data Generation

摘要： 监管机构共享微数据，如基金持仓和衍生工具，由于严格的数据保密和隐私法规，面临独特的挑战。 这些挑战常常阻碍学术界和实践者有效进行协作研究。 生成模型的出现，特别是能够合成模仿现实世界数据基础分布的数据的扩散模型，提供了一个有吸引力的解决方案。 本工作介绍了“FinDiff”，一种设计用于生成真实世界金融表格数据的扩散模型，适用于多种监管下游任务，例如经济情景建模、压力测试和欺诈检测。 该模型使用嵌入编码来对包含分类和数值属性的混合模式金融数据进行建模。 FinDiff在生成合成表格金融数据方面的性能通过三个真实世界金融数据集（包括两个公开数据集和一个专有数据集）与最先进的基线模型进行了评估。 实证结果表明，FinDiff在生成高保真度、隐私性和实用性均高的合成表格金融数据方面表现出色。 显示英文摘要

摘要： The sharing of microdata, such as fund holdings and derivative instruments, by regulatory institutions presents a unique challenge due to strict data confidentiality and privacy regulations. These challenges often hinder the ability of both academics and practitioners to conduct collaborative research effectively. The emergence of generative models, particularly diffusion models, capable of synthesizing data mimicking the underlying distributions of real-world data presents a compelling solution. This work introduces 'FinDiff', a diffusion model designed to generate real-world financial tabular data for a variety of regulatory downstream tasks, for example economic scenario modeling, stress tests, and fraud detection. The model uses embedding encodings to model mixed modality financial data, comprising both categorical and numeric attributes. The performance of FinDiff in generating synthetic tabular financial data is evaluated against state-of-the-art baseline models using three real-world financial datasets (including two publicly available datasets and one proprietary dataset). Empirical results demonstrate that FinDiff excels in generating synthetic tabular financial data with high fidelity, privacy, and utility.