Constrained Tabular Diffusion Generates Compliant Financial Data

Generative models in finance face a unique challenge: they must not only produce realistic data but also satisfy stringent regulatory and economic requirements. Standard tabular diffusion models typically lack the capability to enforce these hard constraints. To address this, researchers introduce Constrained Tabular Diffusion for Finance (CTDF). This innovative approach integrates a training-free feasibility operator directly into the reverse-diffusion sampling loop. This allows CTDF to enforce strict constraints during data generation. Extensive experiments on large-scale financial datasets demonstrate that CTDF achieves zero constraint violations, ensuring legal compliance and adherence to economic objectives. It also improves data utility, particularly for scarce data scenarios, establishing a robust method for creating trustworthy and compliant synthetic financial data.