# Synthetic Data Applications in Finance

arXiv: https://arxiv.org/abs/2401.00081 · HTML: https://mlsanity.com/paper/2401.00081/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2023.

## Built on by (most cited first, 22 of 22)

- [ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Models](https://mlsanity.com/paper/2405.17724/) (2024)
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- [Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities](https://mlsanity.com/paper/2405.20959/) (2024)
- [Opportunities and Challenges of Generative-AI in Finance](https://mlsanity.com/paper/2410.15653/) (2024)
- [Escaping Model Collapse via Synthetic Data Verification: Near-term Improvements and Long-term Convergence](https://mlsanity.com/paper/2510.16657/) (2025)
- [TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure](https://mlsanity.com/paper/2602.23784/) (2026)
- [Give me Some Hard Questions: Synthetic Data Generation for Clinical QA](https://mlsanity.com/paper/2412.04573/) (2024)
- [Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chance](https://mlsanity.com/paper/2501.03993/) (2025)
- [Adaptive and Robust Watermark for Generative Tabular Data](https://mlsanity.com/paper/2409.14700/) (2024)
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- [Towards More Accurate US Presidential Election via Multi-step Reasoning with Large Language Models](https://mlsanity.com/paper/2411.03321/) (2024)
- [Chase Anonymisation: Privacy-Preserving Knowledge Graphs with Logical Reasoning](https://mlsanity.com/paper/2410.12418/) (2024)
- [Auditing and Enforcing Conditional Fairness via Optimal Transport](https://mlsanity.com/paper/2410.14029/) (2024)
- [InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior](https://mlsanity.com/paper/2507.06528/) (2025)
- [Watermarking Generative Categorical Data](https://mlsanity.com/paper/2411.10898/) (2024)
- [Generating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraints](https://mlsanity.com/paper/2505.05019/) (2025)
- [AutoGeTS: Knowledge-based Automated Generation of Text Synthetics for Improving Text Classification](https://mlsanity.com/paper/2508.10000/) (2025)
- [Hybrid Data can Enhance the Utility of Synthetic Data for Training Anti-Money Laundering Models](https://mlsanity.com/paper/2509.18499/) (2025)
- [Privacy-Aware Time Series Synthesis via Public Knowledge Distillation](https://mlsanity.com/paper/2511.00700/) (2025)
- [RaMark: Radioactive Watermarking for Generated Tabular Data](https://mlsanity.com/paper/2607.09000/) (2026)
- [FlowLOB: Efficient and Controllable Limit Order Book Generation with Flow Matching](https://mlsanity.com/paper/2608.13096/) (2026)

## Abstract (arXiv)

Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applications of synthetic data in the financial sector and in particular provide richer details for a few select ones. These cover a wide variety of data modalities including tabular, time-series, event-series, and unstructured arising from both markets and retail financial applications. Since finance is a highly regulated industry, synthetic data is a potential approach for dealing with issues related to privacy, fairness, and explainability. Various metrics are utilized in evaluating the quality and effectiveness of our approaches in these applications. We conclude with open directions in synthetic data in the context of the financial domain.