Synthetic Financial

Synthetic financial data generation focuses on creating realistic, privacy-preserving substitutes for real financial transactions, aiming to improve model training and evaluation while protecting sensitive information. Current research emphasizes the use of generative adversarial networks (GANs) and variational autoencoders (VAEs), along with techniques like differential privacy, to achieve high fidelity and utility in synthetic datasets for tasks such as fraud detection, risk assessment, and demand forecasting. This field is crucial for advancing financial modeling, enabling researchers to overcome data scarcity and privacy limitations while ensuring the reliability and robustness of machine learning models in the financial sector.