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

Approved

Relevance and Originality This research makes a timely contribution to financial data management by addressing the dual challenges of data valuation and privacy-preserving synthesis. It responds to growing demands on financial institutions to manage increasing data volumes while meeting regulatory standards. By using AI to assess data value during critical transitions like mergers or cloud migration, the study offers practical relevance. The application of generative AI for synthetic dataset creation is a novel approach that supports both privacy and model development, bridging a notable gap in current financial analytics research.Methodology The use of feature importance and clustering techniques provides a grounded, data-driven approach to valuing financial data. These methods reflect real-world business use cases and support automation in data handling. The integration of generative models to synthesize privacy-safe datasets further strengthens the methodology. While overall sound, the research would benefit from more detail on specific algorithms used, data sources, and evaluation metrics to enhance transparency and reproducibility.Validity & Reliability Findings are relevant to key financial areas like fraud detection and credit risk, suggesting strong applicability. The inclusion of bias mitigation, validation processes, and governance shows thoughtful consideration of model reliability. However, further discussion on the long-term performance of synthetic data under different market scenarios could enhance confidence in generalizability. The emphasis on aligning AI strategies with regulatory compliance adds to the robustness of the conclusions.Clarity and Structure The article is logically organized, progressing clearly from problem to solution and implementation. It maintains a balance between technical depth and readability, making it suitable for diverse audiences. Coverage of practical considerations like governance and best practices enhances its utility. However, additional examples of real-world application or comparative studies could improve clarity on the effectiveness of the proposed methods.Result Analysis The study offers a clear, well-supported analysis linking AI-driven data strategies with improved model performance and regulatory outcomes. Its conclusions are firmly grounded in the described approaches, reinforcing the value of adopting these technologies in modern financial systems.