Machine Learning and Generative AI for Data Valuation and Synthesis in Finance

Approved

Relevance and Originality This research highlights a critical and contemporary issue in financial data management by exploring how machine learning and generative AI can address both data valuation and privacy preservation. The dual focus responds directly to the challenges faced by institutions managing large-scale data under tight regulatory constraints. The use of AI for automated value assessment and synthetic data generation introduces a compelling and original angle, especially in contexts such as cloud migration and M&A activity. This cross-section of data utility and compliance adds meaningful value to ongoing conversations around financial system modernization.Methodology The study employs feature importance and clustering algorithms to assess data value based on actual usage and regulatory impact, which reflects a practical and scalable approach. The use of generative AI for creating synthetic datasets enhances privacy while retaining statistical relevance, offering a strong complement to the valuation techniques. Although the chosen methods are well-aligned with the study’s goals, further details on model selection, dataset size, and evaluation protocols would enhance methodological transparency and credibility.Validity & Reliability The findings are grounded in real-world applications such as fraud detection and credit risk modeling, suggesting strong relevance and practical utility. The emphasis on bias prevention, statistical rigor, and governance frameworks supports the reliability of outcomes. However, the extent to which synthetic datasets maintain performance across diverse financial scenarios could be more explicitly addressed. Strengthening these aspects would improve the overall robustness and generalizability of the conclusions.Clarity and Structure The article presents a clear and logical progression from problem identification to solution and implementation, with thoughtful integration of both technical and regulatory perspectives. The writing is precise and accessible, ensuring clarity for interdisciplinary readers. Implementation challenges and best practices are effectively included, though real-world case studies or deployment outcomes would offer additional context and enhance the practical relevance of the discussion.Result Analysis The study provides a convincing interpretation of how machine learning and generative AI can elevate model accuracy and ensure regulatory alignment. The conclusions are well-supported by the described techniques and reinforce the growing importance of data-driven, privacy-conscious strategies in financial decision-making.