Research on Anti-Money Laundering Suspicious Transaction Monitoring System Based on Deep Learning

Abstract

With the development of financial technology, money laundering activities have become diversified and concealed. Traditional anti-money laundering (AML) monitoring methods are struggling to meet current regulatory requirements. This paper proposes an anti-money laundering suspicious transaction monitoring system based on deep learning. The system collects transaction information and personal data in real-time, and utilizes Generative Adversarial Networks (GANs) and rule systems for in-depth analysis of transaction data to identify and warn of suspicious money laundering activities. The study aims to enhance the accuracy and efficiency of AML monitoring, providing strong technical support for financial institutions.