This objective of this VM is to prepare the paper tackling directly one of the Action deliverables. It aims to bridge the gap between AI-driven models and the application of network data analysis in quantitative finance, offering insights for researchers and practitioners in the field. It will explore the development of AI models aimed at generating synthetic data for failed trials of investment product producers. Additionally, it will delve into quantitative strategies leveraging network data analysis, emphasizing the potential of this emerging field in financial market analysis and investment strategies. By integrating AI driven approaches with data analysis, the paper will highlight novel quantitative strategies that can enhance predictive accuracy and decision-making in finance. Moreover, the study will address the challenges and ethical considerations associated with synthetic data generation, ensuring that the methodologies proposed are both innovative and responsible. This interdisciplinary approach will provide the suggestions how financial data is utilized, driving more informed and effective investment strategies.