Financial fraud costs the world billions of dollars every year, with criminals exploiting financial systems to launder money, commit fraud, and finance illicit activities. While machine learning models have been widely used to detect suspicious transactions, they often require centralized data collection, posing serious risks to data privacy and regulatory compliance. To address this challenge, researchers have developed Fed-RD, a privacy-preserving Federated Learning (FL) model designed specifically for financial crime detection.
Fed-RD allows multiple financial institutions to collaborate on fraud detection without sharing raw transaction data. By leveraging Differential Privacy (DP) and Secure Multiparty Computation (MPC), the model ensures that sensitive financial information remains protected while still enabling accurate anomaly detection. Unlike traditional FL approaches, which typically assume either horizontal or vertical data partitioning, Fed-RD is designed to handle both simultaneously, making it highly applicable to real-world financial systems where transaction data is distributed across different institutions.
Learn more about the study here: https://arxiv.org/html/2408.01609v1#S8
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