Exploring Synthetic Data in Financial Analytics: A Bibliometric Review of Generation Methods, Validation Challenges, and Future Directions
Synthetic data generation has become a strategic capability in financial analytics, promising to reconcile the sector’s appetite for data-hungry machine learning with strict privacy, confidentiality, and regulatory constraints. This review provides a structured, evidence-based account of the field t...
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| Autores principales: | , , , , , , |
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| Formato: | Reference entry |
| Lenguaje: | inglés |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | Enlace permanente (DOI o Handle) |
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| Sumario: | Synthetic data generation has become a strategic capability in financial analytics, promising to reconcile the sector’s appetite for data-hungry machine learning with strict privacy, confidentiality, and regulatory constraints. This review provides a structured, evidence-based account of the field that is both conceptual and empirical. On the conceptual side, it explains how the principal generators work, states their objectives formally, and derives the practical consequences of each mechanism for financial data; it then organises evaluation into an operational fidelity–utility–privacy framework, extends it to incorporate the fairness and model-risk requirements imposed by regulated lending and insurance and the validation constraints that differ by data type, and offers explicit guidance on selecting a generator. On the empirical side, it maps the intellectual structure of the field through a science-mapping study of 313 Scopus-indexed documents (1997–2026) retrieved with a four-block Boolean query executed on 27 July 2026 and screened from 591 retrieved records. Performance analysis reveals sustained exponential growth, at an annual rate of 14.1% to 17.5% depending on how the endpoints are set, with 2026 covering only the first 208 days of the year, and 73.8% of output published since 2023. The corpus has accumulated 4359 citations with an h-index of 35 and pronounced inequality (Gini = 0.737; 26.2% of documents uncited), although 96.3% of the uncited documents appeared in 2025 or 2026 and the Gini falls to 0.599 once the analysis is restricted to documents with at least eighteen months of citation exposure, so these figures reflect the citation window as much as scientific impact. Co-word analysis, a strategic thematic map computed from the equivalence index, and an unsupervised clustering pipeline validated using the silhouette, Davies–Bouldin, and Calinski–Harabasz indices, together with seed, subsample, and specification stability tests, identify five overlapping and provisional research fronts: risk modelling and Monte Carlo simulation; fraud and credit-risk detection under class imbalance; privacy-preserving generation; machine-learning analytics and benchmarking; and biomedical simulation methods transferable to finance. The internal validation indices are weak in absolute terms, so these fronts are best read as an exploratory summary of a densely connected intellectual space rather than as sharply delineated lines of enquiry. We conclude that the field’s binding constraint has shifted from generation to certification: realistic financial data can now be produced, but the evidence that would establish a given synthetic dataset as simultaneously realistic, useful and safe—downstream performance on representative financial tasks, quantified disclosure risk, and robustness across market regimes—is still assembled on a study-by-study basis rather than through shared benchmarks. |
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