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

Approved

Relevance and Originality The research presents a timely and original contribution to the intersection of machine learning, generative AI, and financial data management. It addresses a critical gap in existing financial data practices by focusing on data valuation and privacy-preserving synthesis—two underexplored yet increasingly vital areas in the context of modern regulatory demands and exponential data growth. The exploration of AI-driven methodologies for assessing data utility and generating synthetic datasets reflects a novel approach with practical relevance. The inclusion of real-world financial applications like credit risk, fraud detection, and stress testing enhances the article’s applied significance. Keywords: machinelearning financialdatamanagement dataprivacy syntheticdata datavaluationMethodology The article outlines a comprehensive and well-structured approach to addressing the dual challenge of data valuation and synthetic data generation. The use of feature importance analysis and clustering algorithms for valuation purposes demonstrates methodological rigor. The application of generative AI techniques for producing statistically faithful yet privacy-safe datasets is a practical solution to growing privacy concerns. However, the discussion could benefit from more clarity around the experimental setup, specific tools or models employed, and how performance metrics were calculated to ensure replicability. Keywords: featureimportance clusteringalgorithms generativeAI privacypreservingdataValidity & Reliability The findings appear robust and grounded in both technical feasibility and domain-specific needs. The alignment between machine learning outputs and regulatory or business requirements supports the credibility of the proposed framework. The discussion of validation frameworks and bias prevention mechanisms further strengthens reliability. Nonetheless, generalizability across diverse financial institutions or markets may need elaboration, particularly in relation to governance and infrastructural differences. Keywords: datavalidation regulatorycompliance biasmitigation modelreliabilityClarity and Structure The article is clearly organized, presenting complex concepts in a structured and accessible manner. Each thematic component—data valuation, synthetic generation, implementation challenges—is logically sequenced and well-argued. The readability is enhanced by clear transitions between problem identification, technical solutions, and strategic recommendations. To further improve clarity, visual aids or structured case studies could support key points. Keywords: logicalflow readability contentorganization financialAIgovernanceResult Analysis The result interpretation is thoughtful and connects technical outputs with practical implications. The benefits in areas such as risk assessment, stress testing, and system modernization are well-articulated and supported by performance improvements. The inclusion of implementation challenges and governance practices adds depth and realism to the analysis. Keywords: resultinterpretation modelperformance stress-testing decisionintelligence financialinnovation.