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Synthetic Data Applications in Finance

arXiv 2401.00081 ↗First posted 29 Dec 2023cs.LG q-fin.GNcited by 22

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 10; 2025: 9; 2026: 3.

22 papers.

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  1. 2024ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Modelscited by 28
  2. 2024A Large-Scale Simulation on Large Language Models for Decision-Making in Political Sciencecited by 10
  3. 2025Joint Relational Database Generation via Graph-Conditional Diffusion Modelscited by 9
  4. 2024Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilitiescited by 8
  5. 2024Opportunities and Challenges of Generative-AI in Financecited by 6
  6. 2025Escaping Model Collapse via Synthetic Data Verification: Near-term Improvements and Long-term Convergencecited by 6
  7. 2026TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructurecited by 6
  8. 2024Give me Some Hard Questions: Synthetic Data Generation for Clinical QAcited by 5
  9. 2025Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chancecited by 4
  10. 2024Adaptive and Robust Watermark for Generative Tabular Datacited by 3
  11. 2025GReaTER: Generate Realistic Tabular data after data Enhancement and Reductioncited by 3
  12. 2024Towards More Accurate US Presidential Election via Multi-step Reasoning with Large Language Modelscited by 2
10 more
  1. 2024Chase Anonymisation: Privacy-Preserving Knowledge Graphs with Logical Reasoningcited by 1
  2. 2024Auditing and Enforcing Conditional Fairness via Optimal Transportcited by 1
  3. 2025InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behaviorcited by 1
  4. 2024Watermarking Generative Categorical Datacited by 0
  5. 2025Generating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraintscited by 0
  6. 2025AutoGeTS: Knowledge-based Automated Generation of Text Synthetics for Improving Text Classificationcited by 0
  7. 2025Hybrid Data can Enhance the Utility of Synthetic Data for Training Anti-Money Laundering Modelscited by 0
  8. 2025Privacy-Aware Time Series Synthesis via Public Knowledge Distillationcited by 0
  9. 2026RaMark: Radioactive Watermarking for Generated Tabular Datacited by 0
  10. 2026FlowLOB: Efficient and Controllable Limit Order Book Generation with Flow Matchingcited by 0