Federated Learning Framework for Cross-Institution Transaction Velocity and Anomaly Detection in KYC Systems

As financial institutions around the world are being subject to increasing regulatory requirements and the challenge to identify fraudulent activities grows, there has been a need for secure and efficient scalable solutions in Know Your Customer (KYC) processes like never before. Traditional techniques for Know Your Customer (KYC) systems based on centralized data and static regulation are not up to the task of dealing with the complexities of cross-institutional collaboration and dynamic regulatory environments. This paper proposes the design of a Federated Learning Framework for cross-institution transaction velocity, and anomaly detection in KYC systems that enables privacy preserving decentralised learning in the context of cross-institution learning with data security and regulatory compliance concerning jurisdictional regulations.

The framework combines federated learning (FL) with anomaly detection algorithms and transaction velocity analysis in order to improve unstable transactions (i.e., suspicious activity) in real time without exchanging sensitive data. Using blockchain technology for secure identity management and Self-Sovereign Identity (SSI) framework for decentralized data verification, this system ensures that customer data is private and secure. Our outcomes indicate that the framework is significant in enhancing the accuracy of detecting anomalies and verification transactions than the traditional centralized system.

By exploiting the power of federated learning, financial institutions can work together and exchange insights without compromising customer data privacy. This research makes a contribution to the growing body of knowledge to privacy-preserving machine learning, and its application in KYC systems by proposing a scalable and secure solution for modern financial institutions to fight against money laundering and fraud in a globalized financial system.