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

Approved

Relevance and Originality The research addresses a highly relevant problem in today’s financial landscape: how to extract value from vast amounts of data while maintaining regulatory compliance and privacy. By integrating machine learning for data valuation and generative AI for privacy-preserving synthesis, the article introduces a forward-thinking and original approach. Its focus on use cases like mergers, cloud migration, and regulatory-driven modernization reflects real industry pressures, offering practical value. The combined use of data-driven insights and synthetic data generation represents a novel direction that advances current practices in financial data strategy.Methodology The study applies machine learning methods such as feature importance analysis and clustering to assess data based on patterns of use, business impact, and compliance relevance. This structured, algorithmic approach supports scalable and objective data evaluation. The use of generative AI to produce synthetic datasets that mirror statistical properties of original financial data adds a valuable privacy-preserving layer. However, a more detailed explanation of the model architectures, data sources, and validation steps would further enhance the transparency and reproducibility of the research design.Validity & Reliability Findings are grounded in key financial applications such as fraud detection, credit scoring, and stress testing, reinforcing their practical reliability. The article’s attention to bias prevention, validation frameworks, and statistical rigor strengthens its trustworthiness. Still, the robustness of synthetic data in capturing edge cases or rare events—often critical in finance—could benefit from deeper discussion. Addressing limitations in performance consistency across varied data conditions would add further confidence in the generalizability of the approach.Clarity and Structure The article is well-organized, with a clear progression from problem statement to solution and future implications. The integration of technical methods and governance considerations is handled smoothly, making the content accessible without sacrificing depth. The inclusion of implementation challenges and best practices adds a grounded, actionable layer. That said, incorporating examples from pilot deployments or system-level benchmarks could make the insights more relatable for practitioners and decision-makers.Result Analysis The research presents a compelling case for how AI-enabled data valuation and synthetic generation can improve model performance and regulatory readiness. The conclusions align closely with the described methods and suggest meaningful benefits for institutions seeking to modernize while maintaining data control and privacy.