Federated and Privacy-Preserving MLOps Frameworks: Blockchain-Enabled Compliance for KYC in Financial Systems

Abstract

Federated learning (FL), privacy-preserving machine learning (PPML), and blockchain technologies present an opportunity to improve Know Your Customer (KYC) compliance in the financial systems and ensure that the privacy of the user is protected. Regardless of this growing interest, the current frameworks usually do not meet the complex regulatory demands, scalability issues and identity attestation demands of real-world financial systems. Based on an integrative review approach, articles and the latest frameworks of the credible data sources are examined to pinpoint the architectural patterns, privacy strategies, governing models, and compliance characteristics are examined. Study found out that there are enduring privacy and auditability tradeoffs, a bottleneck in large-scale deployments, and gaps in full regulatory alignment, especially in the areas of credential revocation and lawful deanonymization. Review emphasises the recent practical models like REGKYC and DPFedBank, which show the progress of policy implementation and privacy-utility ratios. Considering these insights, we will make our recommendations based on the following provisions: modular architecture, flexible identity credential systems, the clarity of governance, adjustable regulatory provisions, and user-friendly privacy mechanisms. This paper highlights the necessity of field pilot applications and cross-functional cooperation to move federated privacy-preserving MLOps out of the prototype into a working application in financial KYC. This review can be seen as a contribution to the further development of blockchain-based compliance tools that will provide safe and transparent, and privacy-sensitive identity management in the changing financial industry.