A Hybrid Approach for Detecting Suspicious Accounts in Money Laundering Using Data Mining Techniques

Abstract

Money laundering is a criminal activity to disguise black money as white money. It is a process by which illegal funds and assets are converted into legitimate funds and assets. Money Laundering occurs in three stages: Placement, Layering, and Integration. It leads to various criminal activities like Political corruption, smuggling, financial frauds, etc. In India there is no successful Anti Money laundering techniques which are available. The Reserve Bank of India (RBI), has issued guidelines to identify the suspicious transactions and send it to Financial Intelligence Unit (FIU). FIU verifies if the transaction is actually suspicious or not. This process is time consuming and not suitable to identify the illegal transactions that occurs in the system. To overcome this problem we propose an efficient Anti Money Laundering technique which can able to identify the traversal path of the Laundered money using Hash based Association approach and successful in identifying agent and integrator in the layering stage of Money Laundering by Graph Theoretic Approach.