FinDiff: Diffusion Models for Financial Tabular Data Generation

TL;DR

FinDiff is a diffusion model that synthesizes high-fidelity, privacy-preserving financial tabular data, enabling regulatory and research applications while outperforming existing models.

Contribution

This paper introduces FinDiff, a novel diffusion-based generative model specifically designed for complex financial tabular data with mixed modalities.

Findings

FinDiff outperforms baseline models in data fidelity.

The model maintains high privacy and utility levels.

Effective across multiple real-world financial datasets.

Abstract

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.