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International Journal for Research Trends and Innovation
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Paper Title: Detection of Suspicious Account using Data Mining Techniques
Authors Name: Amruta Hanumant Raut , S V. Athawale
Unique Id: IJRTI1804040
Published In: Volume 3 Issue 4, April-2018
Abstract: Money Laundering is criminal activity to disguise black money as white money. Money Laundering is process of converting illegal asset or funds into legitimate or legal asset or funds. Data mining techniques are very efficient techniques to determine suspicious accounts in Money Laundering .Data mining is task or procedure of analyzing large amount of data stored in Database and finding correlation or patterns among that amount of data. Different anti money laundering techniques are used for finding suspicious transaction of money and this data is send to Financial Intelligence Unit (FIU). Financial Intelligence Unit verifies if transaction is actually suspicious or not. Hash based Association mining is very useful technique for this purpose. An efficient anti Money Laundering technique called Hash Based Association can be able to identify the traversal path of the Laundered Money .Most international financial institutions have been implementing traditional investigative techniques which are more time consuming. Efficiency can be increase by generating frequent transactional datasets or patterns and this will then be used in the graph theoretic approach to identify the traversal path of suspicious transactions. We will improve the performance of current solution in terms of running time and provide support in finding agent and integrator in transaction path successfully using data mining approach.
Keywords: Money Laundering, Hash Based Association Mining, Frequent- 2 Item Set, Data Mining, Traversal Path
Cite Article: "Detection of Suspicious Account using Data Mining Techniques ", International Journal of Science & Engineering Development Research (, ISSN:2455-2631, Vol.3, Issue 4, page no.212 - 218, April-2018, Available :
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Publication Details: Published Paper ID: IJRTI1804040
Registration ID:180092
Published In: Volume 3 Issue 4, April-2018
DOI (Digital Object Identifier):
ISSN Number: 2456 - 3315
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