International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 8, No. 5, 8 May 2016
Cover page and Table of Contents: PDF (size: 548KB)
A Hybrid Approach for Detecting Suspicious Accounts in Money Laundering Using Data Mining Techniques
Full Text (PDF, 548KB), PP.37-43
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Author(s)
Ch. Suresh
1,*
K. Thammi Reddy
1
N. Sweta
1
Index Terms
Data mining, Anti Money Laundering, FIU, Hash Based Mining, Traversal Path
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.
Cite This Paper
Ch.Suresh, K.Thammi Reddy, N. Sweta, "A Hybrid Approach for Detecting Suspicious Accounts in Money Laundering Using Data Mining Techniques", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.5, pp.37-43, 2016. DOI:10.5815/ijitcs.2016.05.04
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