Constrained Tabular Diffusion for Finance

• URL: http://arxiv.org/abs/2606.28674v1
• Date: Sat, 27 Jun 2026 01:27:35 GMT
• Title: Constrained Tabular Diffusion for Finance
• Abstract summary: Generative models in finance face the dual challenge of producing realistic data while satisfying strict regulatory and economic objectives.<n>We introduce Constrained Tabular Diffusion for Finance (CTDF), a novel integration of sampling-time feasibility operations with mixed-type tabular diffusion.<n>CTDF enforces hard constraints for applications such as simulation, legal compliance, and extrapolation.
• Score: 1.1728348229595655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative models in finance face the dual challenge of producing realistic data while satisfying strict regulatory and economic objectives, a requirement that standard tabular diffusion models cannot provide. To address this difficulty, we introduce Constrained Tabular Diffusion for Finance (CTDF), a novel integration of sampling-time feasibility operations with mixed-type tabular diffusion in financial applications. By incorporating a training-free feasibility operator into the reverse-diffusion sampling loop, CTDF enforces hard constraints for applications such as simulation, legal compliance, and extrapolation. Extensive experiments on large-scale financial datasets demonstrate zero constraint violations and improvement in scarce data utility. CTDF establishes a robust method for generating trustworthy and compliant synthetic data, opening new avenues for rigorous generative modeling and analysis in the financial domain.

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