Synthetic Financial Data Generation Using Generative AI: Privacy, Security and Model Reliability in Modern Banking
Author(s): M Ravi Kumar
Publication #: 2609007
Date of Publication: 09.09.2025
Country: India
Pages: 1-5
Published In: Volume 11 Issue 5 September-2025
Abstract
The rapid digital transformation of the banking sector has created an increasing demand for large, diverse and high-quality financial datasets for artificial intelligence, fraud detection, credit-risk assessment and financial modelling. However, the use of real customer data creates significant privacy, security and regulatory challenges. Synthetic financial data generated using Generative Artificial Intelligence (GenAI) provides an emerging solution by producing artificial datasets that preserve important statistical characteristics of real financial data without directly exposing individual customer records. This paper examines the role of Generative AI in synthetic financial data generation, with particular emphasis on privacy protection, security, data utility and model reliability in modern banking. Generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion models and Large Language Models (LLMs) can generate synthetic transaction and customer datasets for model development and testing.
However, synthetic data does not automatically guarantee privacy, as models may reproduce sensitive information or create disclosure risks. Therefore, privacy evaluation, data-quality assessment, bias detection and model validation are essential. The paper proposes a framework combining data generation, privacy assessment, security controls, utility evaluation and human governance. The present research states that synthetic financial data can support responsible AI adoption in banking when supported by strong privacy, security, validation and regulatory governance mechanisms.
Keywords: Generative AI, Synthetic Data, Banking, Financial Data, Privacy, Cybersecurity, GAN, Data Security, Model Reliability, Artificial Intelligence.
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