# FinDiff: Diffusion Models for Financial Tabular Data Generation

**Authors:** Timur Sattarov, Marco Schreyer, Damian Borth  
**arXiv:** [2309.01472](https://arxiv.org/abs/2309.01472)  
**Date:** 2023-09-04  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.01472v1/

## Key points

- Strict regulations on data confidentiality and privacy pose a challenge for sharing sensitive financial microdata
- Generative models, specifically diffusion models like 'FinDiff', offer a promising solution for synthesizing authentic financial datasets
- FinDiff, developed by Timur Sattarov, Marco Schreyer, and Damian Borth, is a meticulously crafted model suitable for tasks such as economic scenario modeling, stress tests, and fraud detection
- FinDiff adeptly captures the mixed modality nature of financial data with both categorical and numeric attributes using embedding encodings
- Empirical evaluation against state-of-the-art baseline models demonstrates that FinDiff excels in generating synthetic tabular financial data with high fidelity while ensuring privacy protection and utility preservation

## Summary

In the realm of financial data generation, strict regulations on data confidentiality and privacy pose a significant challenge for sharing sensitive microdata such as fund holdings and derivative instruments. This often hinders collaboration between academics and industry practitioners. However, a promising solution has emerged in the form of generative models, specifically diffusion models. These models have the ability to synthesize data that closely resembles real-world financial datasets. One such model is 'FinDiff', developed by Timur Sattarov, Marco Schreyer, and Damian Borth. FinDiff is a meticulously crafted diffusion model that generates authentic financial tabular data suitable for various regulatory tasks like economic scenario modeling, stress tests, and fraud detection. By leveraging embedding encodings, FinDiff adeptly captures the mixed modality nature of financial data encompassing both categorical and numeric attributes. To assess its efficacy, FinDiff underwent rigorous evaluation against state-of-the-art baseline models using three distinct real-world financial datasets - including two publicly available sets and one proprietary dataset. The empirical results unequivocally demonstrate that FinDiff excels in generating synthetic tabular financial data with exceptional fidelity while maintaining high standards of privacy protection and utility preservation. This groundbreaking work not only addresses the pressing challenges associated with sharing sensitive financial microdata but also paves the way for enhanced collaboration and innovation in the field of finance through the development of advanced generative models like FinDiff.