FinDiff: Diffusion Models for Financial Tabular Data Generation

ArXiv ID: 2309.01472 “View on arXiv”

Authors: Unknown

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.

Keywords: diffusion models, generative models, synthetic data generation, financial tabular data, privacy preservation, General (Financial Data)

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts from diffusion models and latent space embeddings, requiring dense theoretical derivations; it also demonstrates high empirical rigor through rigorous testing on multiple financial datasets with clear fidelity, privacy, and utility metrics.

  flowchart TD
    A["Research Goal<br>Generate synthetic financial<br>tabular data preserving privacy"] --> B["Input Data<br>3 Real-World Datasets<br>2 Public, 1 Proprietary"]
    B --> C["Methodology<br>FinDiff: Diffusion Model<br>with Embedding Encodings"]
    C --> D["Computation<br>Training on Mixed<br>Categorical & Numeric Data"]
    D --> E["Generation<br>Sampling Synthetic Data"]
    E --> F["Key Findings<br>High Fidelity, Privacy, Utility"]