Integrating blockchain and machine learning for enhanced anti-money laundering system

Money laundering is a serious threat to global financial systems, causing instability and inflation, and especially hurting middle-class savings. This paper suggests a new way to tackle these problems by using blockchain technology and advanced machine learning models. We use hyperledger fabric to securely record transactions and advanced algorithms like autoencoders and neural networks to create a strong anti-money laundering (AML) system. This system can detect and predict illegal financial activities in real-time and includes continuous monitoring and alerts. These features improve financial transparency and stability while protecting the savings of the middle class. By identifying and reducing risks early, our solution not only ensures compliance with regulations but also strengthens the resilience of global financial systems against new threats. This research helps develop effective ways to fight financial crime, promoting a safer and more transparent financial world.