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Money laundering is a criminal activity to\uddisguise black money as white money. It is a process by\udwhich illegal funds and assets are converted into\udlegitimate funds and assets. Money Laundering occurs in\udthree stages: Placement, Layering, and Integration. It\udleads to various criminal activities like Political\udcorruption, smuggling, financial frauds, etc. In India\udthere is no successful Anti Money laundering techniques\udwhich are available. The Reserve Bank of India (RBI),\udhas issued guidelines to identify the suspicious\udtransactions and send it to Financial Intelligence Unit\ud(FIU). FIU verifies if the transaction is actually\udsuspicious or not. This process is time consuming and not\udsuitable to identify the illegal transactions that occurs in\udthe system. To overcome this problem we propose an\udefficient Anti Money Laundering technique which can\udable to identify the traversal path of the Laundered\udmoney using Hash based Association approach and\udsuccessful in identifying agent and integrator in the\udlayering stage of Money Laundering by Graph Theoretic\udApproach.\u

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