Identification of Money Laundering based on Financial Action Task Force Using Transaction Flow Analysis System
Abstract
Money laundering transaction is to be identified in the real world financial application; a new method can be proposed for detection. In this system, a laundering detection is based on the social network using transaction flow analysis system wetop-quality an appropriate classifying strategy to determine typical money laundering patterns and money laundering rules. From that state, we can quickly identify the abnormal transaction data. A part of a larger chain transactions in money laundering is to be such risk. For to overcome that risk we use a social network to connect missing links in potential transaction sequences. A financial sector independent risk assessment can be provided to submit the transaction. The potential participants can be connected to a social network. The transformation of vast quantities of data into a huge number of reports is not a perfect detection for to overcome that we use a Transaction Flow Analysis. From distributive box and collective box, the transaction mining system is to detect the money laundering. In this system, we can also use a Financial Action Task Force (FATF) to avoid a money laundering. From social network analysis money laundering of terrorist financing. We can also detect the criminal activities involve in the money laundering. The FATF can provide a static assessment of money Laundering.
Keywords The Financial Sector, Money Laundering, Potential Transaction, Sequence Mining.