Synthetic Data Generation for Privacy-Preserving Artificial Intelligence in Financial Big Data Ecosystems: A Privacy-Aware Synthetic Intelligence Framework (PASIF)

Pooja Badgujar

doi:10.70528/IJLRP.v7.i7.2346

Synthetic Data Generation for Privacy-Preserving Artificial Intelligence in Financial Big Data Ecosystems: A Privacy-Aware Synthetic Intelligence Framework (PASIF)

Author(s)	Pooja Badgujar
Country	United States
Abstract	The widespread rollout of artificial intelligence (AI), cloud-based data systems, and big data analysis has revolutionized financial services risk management, fraud detection, personalization, and strategic decision making. But all of these solutions require access to vast amounts of accurate, financial information, much of which includes sensitive customer information that is subject to extremely tight privacy and governance standards. Preserving the confidentiality of information while also retaining its analytical utility has emerged as one of the toughest challenges for today's enterprise data ecosystems. While methods like data masking, anonymization, and tokenization have been effective at mitigating disclosure risks, they have been known to compromise data quality, restrict the performance of AI models, and be insufficient in large-scale AI applications to prevent advanced re-identification attacks. Synthetic data generation is a potentially viable solution for creating private AI systems, with recent developments in Generative Artificial Intelligence (Generative AI) and advances in this field. Synthetic data allows businesses to generate statistically representative datasets that mimic the properties of real financial information without revealing customers' actual data, thereby minimizing privacy risks and enhancing regulatory compliance while training, validating, and deploying AI models. Despite this, there is a need for an overarching framework that ensures a good balance of privacy protection, data quality, data governance, and AI performance on enterprise-scale data platforms. In this paper, we present a comprehensive framework for the privacy-preserving AI in financial big data ecosystem, named Privacy-Aware Synthetic Intelligence Framework (PASIF). PASIF unites automated privacy assessment, AI-driven synthetic data generation, differential privacy, metadata-driven governance, continuous data quality monitoring, and lifecycle monitoring in one enterprise framework. The proposed architecture allows organizations to create high-quality synthetic datasets without compromising privacy, regulatory standards or scalability. The proposed framework will decrease privacy exposure by over 90%, cut compliance and data preparation costs by 25-40% and enhance the efficiency of AI development by providing privacy-safe datasets for experimentation and model training by 30-50%. In addition to operational enhancements, PASIF also lays the groundwork for a scalable strategy in responsible AI adoption by incorporating privacy engineering, governance, and ongoing validation throughout the synthetic data lifecycle. It offers a practical blueprint for financial institutions looking to drive AI innovation and build greater customer privacy, regulatory and enterprise resilience in a data-driven financial environment.
Keywords	Synthetic Data, Artificial Intelligence, Privacy-Preserving AI, Differential Privacy, Financial Big Data, Federated Learning, Responsible AI, Enterprise Data Platforms, Data Governance, Privacy Engineering.
Field	Engineering
Published In	Volume 7, Issue 7, July 2026
Published On	2026-07-04
DOI	https://doi.org/10.70528/IJLRP.v7.i7.2346

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Synthetic Data Generation for Privacy-Preserving Artificial Intelligence in Financial Big Data Ecosystems: A Privacy-Aware Synthetic Intelligence Framework (PASIF)

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