Fraud Detection in Anti-money Laundering System Using Machine Learning Techniques

Money laundering (ML) is a process of fabricating large amount of capital gained from any illegal or unethical actions consisting of serious offences such as drug trafficking, mafia, terrorism, etc. By taking its advantages, some fallacious persons transform their illegal source of assets into a legal one. The process of identifying these kinds of money laundering activities is called Anti-Money laundering (AML). This process is becoming more complex in recent years due to the advancement of technology. In recent years, many financial institutions have shown their interest to develop some techniques to fight against various levels of fraud in transactions. Since a large number of transactions take place every day, it is difficult to identify the fraudulent ones manually. In this study, an architectural model has been proposed to determine the fraud patterns in past transactions and to detect the lawless ones in real-time. It is observed that Machine learning techniques help in distinguishing and recognizing these transaction patterns. Eight machine learning techniques like Support Vector Machine (SVM), Logistic regression, Average perceptron, Neural networks, Decision trees, and Random forest are implemented on a set of AML data and observed that random forest works best amongst all.