mlsanityData to 7 Oct 2026

New Money: A Systematic Review of Synthetic Data Generation for Finance

arXiv 2510.26076 ↗First posted 30 Oct 2025cs.LGcited by 5

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Abstract (arXiv)

Synthetic data generation has emerged as a promising approach to address the challenges of using sensitive financial data in machine learning applications. By leveraging generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), it is possible to create artificial datasets that preserve the statistical properties of real financial records while mitigating privacy risks and regulatory constraints. Despite the rapid growth of this field, a comprehensive synthesis of the current research landscape has been lacking. This systematic review consolidates and analyses 72 studies published since 2018 that focus on synthetic financial data generation. We categorise the types of financial information synthesised, the generative methods employed, and the evaluation strategies used to assess data utility and privacy. The findings indicate that GAN-based approaches dominate the literature, particularly for generating time-series market data and tabular credit data. While several innovative techniques demonstrate potential for improved realism and privacy preservation, there remains a notable lack of rigorous evaluation of privacy safeguards across studies. By providing an integrated overview of generative techniques, applications, and evaluation methods, this review highlights critical research gaps and offers guidance for future work aimed at developing robust, privacy-preserving synthetic data solutions for the financial domain.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 32 of its 79 references that are in the map. First posted on arXiv 30 Oct 2025.

Builds on

32 of 79 references.

References · 32

  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2019Modeling Tabular data using Conditional GANcited by 695
  3. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 329
  4. 2018Data Synthesis based on Generative Adversarial Networkscited by 184
  5. 2021CTAB-GAN: Effective Table Data Synthesizingcited by 156
  6. 2023TimeGPT-1cited by 149
  7. 2021TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generationcited by 139
  8. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
  9. 2021DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networkscited by 109
  10. 2019Quant GANs: Deep Generation of Financial Time Seriescited by 107
  11. 2020Tabular Transformers for Modeling Multivariate Time Seriescited by 77
  12. 2019G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminatorscited by 53
20 more
  1. 2022GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networkscited by 43
  2. 2020Generating Realistic Stock Market Order Streamscited by 30
  3. 2021Towards Realistic Market Simulations: a Generative Adversarial Networks Approachcited by 29
  4. 2021OCT-GAN: Neural ODE-based Conditional Tabular GANscited by 27
  5. 2021Towards Generating Real-World Time Series Datacited by 26
  6. 2022Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesiscited by 24
  7. 2020Kamino: Constraint-Aware Differentially Private Data Synthesiscited by 23
  8. 2019Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combinationcited by 22
  9. 2020Survey of explainable machine learning with visual and granular methods beyond quasi-explanationscited by 20
  10. 2019Time Series Simulation by Conditional Generative Adversarial Netcited by 16
  11. 2020Using generative adversarial networks to synthesize artificial financial datasetscited by 10
  12. 2023A Comprehensive Review on Financial Explainable AIcited by 10
  13. 2022Fractional SDE-Net: Generation of Time Series Data with Long-term Memorycited by 9
  14. 2019Style Transfer with Time Series: Generating Synthetic Financial Datacited by 7
  15. 2021Scenario generation for market risk models using generative neural networkscited by 7
  16. 2021Validation Methods for Energy Time Series Scenarios from Deep Generative Modelscited by 5
  17. 2022StyleTime: Style Transfer for Synthetic Time Series Generationcited by 5
  18. 2019Generating Data using Monte Carlo Dropoutcited by 4
  19. 2021Conditional Loss and Deep Euler Scheme for Time Series Generationcited by 4
  20. 2019Second-order Control of Complex Systems with Correlated Synthetic Datacited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Tide: A Customisable Dataset Generator for Anti-Money Laundering Researchcited by 0
  2. 2026Synthetic Tabular Generators Fail to Preserve Behavioral Fraud Patterns: A Benchmark on Temporal, Velocity, and Multi-Account Signalscited by 0
  3. 2026FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Modelscited by 0
  4. 2026Efficient Learning of Deep State Space Models via Importance Smoothingcited by 0
  5. 2026Collaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Siloscited by 0