Handling Smurfing Through Big Data

Abstract

Money laundering is a worrying term for every country’s economy these days. Leading economists of all major developed and developing economies are concerned to devise methods to prevent it. The economy of a country is weakened by the impact of money laundering. Networks created between various banks in different countries facilitate online money transfer, which is turning the process of money laundering into digital money laundering. This promotes money launderers to perform wired transactions from anywhere. People involved in the process of money laundering are efficiently using online banking as their weapon. Evading the anti-money laundering agencies is becoming easier for them because of having online bank accounts. Such people are misusing technology. Therefore, it is restricting one’s own country’s economic progress. But with the help of recently developed technologies, we are able to prevent such illegal activities. Scrutinizing all the transactions and investigating them manually at financial intelligence units are cumbersome tasks because petabytes of transactions are taking place each day. Advanced technologies like Big Data enable us to detect the suspicious customers possibly involved in money laundering. In this paper, we have proposed a methodology using big data to detect smurfing; based on which, suspicious people involved in money laundering may be identified and appropriate action can be taken against them.