Federated Learning for Cross-Organizational Fraud Detection in Financial Services

This paper presents a secure federated learning approach for collaborative fraud detection across multiple financial institutions without sharing sensitive customer data. By aggregating model updates instead of raw data, the system significantly enhances the accuracy of fraud detection models while preserving data privacy and complying with stringent regulatory requirements in the financial sector.