Privacy-Preserving Machine Learning in Financial Customer Data: Trade-Offs Between Accuracy, Security, And Personalization

ABSTRACT : As financial institutions use machine learning (ML) to understand and predict customer behavior, the conflict between data use and privacy is clearer than ever. Financial data is one of the most sensitive types of personal information; it is highly regulated, deeply personal, and often targeted by adversaries. This paper looks at the changing landscape of privacy-preserving machine learning (PPML) in the financial sector, focusing on two main approaches: federated learning (FL) and differential privacy (DP). We examine the trade-offs between model accuracy, data security, and customer personalization, offering a multi-dimensional analysis based on real-world situations and regulatory needs. The study also develops a hybrid framework that combines the strengths of FL and DP, providing a scalable plan for compliant, secure, and effective financial analytics. We draw insights from recent literature, simulations, and case studies in banking applications. This research is particularly relevant as institutions aim to meet data protection laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) without losing their competitive edge in data-driven decision-making.