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

Approved

Relevance and Originality The research offers a forward-looking perspective on how machine learning and generative AI are reshaping financial data management, focusing on the dual needs of data valuation and privacy protection. By aligning data strategies with regulatory demands and institutional shifts like cloud migration, the study addresses a real and pressing challenge in the financial sector. The innovative use of generative AI to produce statistically sound synthetic datasets presents a novel contribution, particularly relevant as financial institutions seek to modernize systems without risking sensitive information.Methodology The combination of feature importance and clustering techniques demonstrates a clear and effective approach to assessing data value in operational contexts. These methods allow for scalable, usage-based insights that are directly tied to business relevance. The generative AI component, used to produce privacy-safe synthetic data, complements the valuation process well. While the overall design is robust, the study would benefit from clearer explanation of the specific algorithms applied and the structure of the validation process to strengthen reproducibility.Validity & Reliability The study’s application to areas like fraud detection, stress testing, and credit risk supports the reliability of its findings. Its attention to statistical rigor, bias mitigation, and validation frameworks shows a commitment to trustworthy outcomes. However, the generalizability of synthetic data across diverse financial environments remains an open question. Further clarification on how performance was measured across different use cases would enhance confidence in the research’s broader applicability.Clarity and Structure The article is well-organized, moving logically from context to application and concluding with practical considerations. The writing is accessible yet maintains technical depth, effectively serving both data science and finance professionals. Including challenges alongside solutions provides a balanced, real-world view. Nonetheless, expanding on implementation details or offering a real-case comparison could improve the depth of insight for practitioners.Result Analysis The study delivers a compelling analysis connecting AI-based data strategies with measurable improvements in model performance and compliance outcomes. Conclusions are well-aligned with the described techniques and point toward clear benefits for institutions operating in a data-regulated environment.