Enhancing Banking Efficiency Through Synthetic Data Generation and Generative AI Applications

The context of this study revolves around synthetic data generation within the realm of generative AI in the banking sector and its impact on operational efficiency, risk management, fraud detection, and regulatory compliance. Research based on Technology-Organization-Environment (TOE) methodologies is being executed to assess what factors influence the adoption of these technologies in banking. The review is contributing to advance the discourse on artificial intelligence-enriched financial innovation, while conceptual modelling sheds light on technological feasibility, organizational readiness, and regulation constraints. The findings suggest that synthetic data enable banks to do data-driven decision-making without losing sensitive customer information, ensuring compliance with the data protection laws while enhancing robust risk behavior predictive models. Generative AI enhances fraud detection capabilities through anomaly detection and predictive analytics, thereby allowing banks to proactively identify suspicious transactions. But data bias, algorithmic transparency, and AI governance raise vested challenges which need to be addressed for responsible implementation of AI. This study proposes a conceptual framework which would introduce an effective reconciliation between financial efficiency and regulatory compliance, thereby augmenting the discourse on AI-driven banking. This study presents a novel point of view regarding the use of synthetic data and generative AI in banking to fill in the breach existing between technological innovations and regulatory requirements. In contrast to existing literature, which generally treats AI applications or financial regulations in isolation, this research brings both dimensions together in an integrated framework for ethical and efficient deployment of AI in financial services.