Machine Learning for Anti-Money Laundering and Fraud Detection

Financial institutions must meet international regulations to ensure not to provide services to criminals and terrorists. They also need to continuously monitor financial transactions to detect suspicious activities. Businesses have many operations that monitor and validate their customer's information against sources that either confirm their identities or disprove. Failing to detect unclean transaction will result in harmful consequences on the financial institution responsible for that such as warnings or fines depending on the transaction severity level. The financial institutions use Anti-money laundering (AML) software sanctions screening and Watch-list filtering to monitor every transaction within the financial network to verify that none of the transactions can be used to do business with forbidden people. Lately, the financial industry and academia have agreed that machine learning (ML) may have a significant impact on monitoring money transaction tools to fight money laundering. Several research work and implementations have been done on Know Your Customer (KYC) systems. To overcome this problem we propose an efficient Anti-Money Laundering System which can able to identify the traversal path of the Laundered money using the Hash-based Association approach and success in identifying agents and integrators in the layering stage of Money Laundering by Graph-Theoretic Approach. Also, detect credit card fraud. Also, it will minimize the compliance officers' effort, and provide faster processing time.