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...

Descripción completa

Detalles Bibliográficos

Autores principales:	Alexander Haro-Sarango, Diana Carolina Castillo-Martínez, Silvia Cachay-Salcedo, Jessica Karina Saavedra-Vasconez, José Campos-Ku, Briseida Sotelo-Castro, Carlos Saavedra-Vasconez
Formato:	Reference entry
Lenguaje:	inglés
Publicado:	2026
Materias:	synthetic data generation financial analytics data privacy synthetic data validation fairness model risk fraud detection class imbalance Monte Carlo simulation risk modeling research frontiers citation analysis co-word analysis science mapping Privacy-Preserving Technologies in Data demographic modeling and climate adaptation Imbalanced Data Classification Techniques
Acceso en línea:	Enlace permanente (DOI o Handle)
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!