Privacy Preserving Machine Learning Framework for Robust Anti-Money Laundering

Abstract

Anti-Money Laundering (AML) has become a major challenge for financial institutions because of the increasing number of digital transactions and the sophistication of money laundering methods. Traditional AML systems depend on centralized data processing, which brings up important issues regarding data privacy, security, and adherence to regulations. This research introduces a Privacy-Preserving Machine Learning framework designed to enhance AML detection while keeping sensitive financial information confidential. The system employs Federated Learning to develop models through collaborative training without sharing raw transaction data. It also integrates Differential Privacy and secure aggregation methods to further protect data. Machine learning algorithms are used to analyze transaction patterns and detect unusual activities efficiently. The results from the experiments show that this framework improves the accuracy of detecting suspicious wwactivities while lowering privacy risks and reducing false alerts. The system provides a secure, scalable, and legally compliant approach for modern AML solutions.