SELECTION OF OPTIMAL MINING ALGORITHM FOR OUTLIER DETECTION - AN EFFICIENT METHOD TO PREDICT/DETECT MONEY LAUNDERING CRIME IN FINANCE INDUSTRY
Journal Title: Elysium Journal of Engineering Research and Management - Year 2014, Vol 1, Issue 1
Abstract
Today, Money Laundering (ML) poses a serious threat and unique challenge to financial institutions. Most of the financial institutions internationally have been implementing Anti-Money Laundering solutions (AML) to fight against money laundering activities. The volume of transaction data in banking is huge and contains a lot of useful information. Detecting money laundering is one of the most valuable information which we can discover from transaction data. Various Data Mining techniques have been applied in Money Laundering detection system used by financial institutions today. Outlier detection is a data mining technique to detect rare events, deviant objects, and exceptions from client or customer transaction data. This paper, discusses on different outlier techniques, comparison between them and framework for selection of right mining algorithm for implementing the same.
Authors and Affiliations
Kannan S, Somasundaram K
Keywords
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