<![CDATA[Data-driven decision-making has taken on a more central role in the banking sector. However, privacy regulations and data security concerns limit the accessibility of real customer data for model training. To address this challenge, synthetic data generation offers a promising solution. This paper presents a framework tool for generating synthetic customer data that closely mimics the statistical properties of real-world data using advanced machine learning techniques, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), to enhance model training in banking applications. By leveraging advanced machine learning techniques, our framework can replicate the real Production Data to Synthetic Data customer. This synthetic data can be used to augment existing datasets, enhance model training, and improve the accuracy and robustness of predictive models. We demonstrate the effectiveness of our framework through a case study in a banking context, showcasing its potential to address challenges related to data privacy, data scarcity, and model performance.]]>
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