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

Abstract

This article explores the transformative role of machine learning and generative AI in financial data management, addressing the dual challenges of data valuation and privacy-preserving synthesis. As financial institutions confront exponential data growth and stringent regulatory requirements, traditional approaches prove increasingly inadequate. Machine learning techniques including feature importance analysis and clustering algorithms enable automated assessment of data value based on usage patterns, business outcomes, and regulatory significance—particularly valuable during organizational transitions like mergers and cloud migrations. Complementing these valuation approaches, generative AI creates synthetic financial datasets that maintain statistical properties of original data without compromising sensitive information. These synthetic alternatives support model development, testing, and collaboration while addressing privacy concerns. Applications span credit risk assessment, fraud detection, stress testing, and system modernization, demonstrating significant improvements in model performance and regulatory compliance. The article examines implementation challenges including statistical rigor, bias prevention, validation frameworks, and governance integration, offering best practices for balancing utility with privacy concerns. Looking forward, financial institutions adopting these technologies stand to benefit from more efficient data management, enhanced decision-making capabilities, and stronger privacy protections in an increasingly data-driven industry.