# Synthetic Data for Robust AI Model Development in Regulated Enterprises

arXiv: https://arxiv.org/abs/2503.12353 · HTML: https://mlsanity.com/paper/2503.12353/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 16 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula](https://mlsanity.com/paper/2212.01375/) (2022)
- [Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data](https://mlsanity.com/paper/2406.13130/) (2024)

## Built on by (most cited first, 1 of 1)

- [Modeling supply chain compliance response strategies based on AI synthetic data with structural path regression: A Simulation Study of EU 2027 Mandatory Labor Regulations](https://mlsanity.com/paper/2505.06261/) (2025)

## Abstract (arXiv)

In today's business landscape, organizations need to find the right balance between using their customers' data ethically to power AI solutions and being compliant regarding data privacy and data usage regulations. In this paper, we discuss synthetic data as a possible solution to this dilemma. Synthetic data is simulated data that mimics the real data. We explore how organizations in heavily regulated industries, such as financial institutions or healthcare organizations, can leverage synthetic data to build robust AI solutions while staying compliant. We demonstrate that synthetic data offers two significant advantages by allowing AI models to learn from more diverse data and by helping organizations stay compliant against data privacy laws with the use of synthetic data instead of customer information. We discuss case studies to show how synthetic data can be effectively used in the finance and healthcare sector while discussing the challenges of using synthetic data and some ethical questions it raises. Our research finds that synthetic data could be a game-changer for AI in regulated industries. The potential can be realized when industry, academia, and regulators collaborate to build solutions. We aim to initiate discussions on the use of synthetic data to build ethical, responsible, and effective AI systems in regulated enterprise industries.