Developing a Methodology to Detect Money Laundering (Using Fuzzy Logic)

Today, crime is rising dramatically in the banking industry with the Growth of information technology in banking network and this Imposes heavy costs on businesses. As a result, crime detection has become a very important issue. One of the crimes that led to disruptions in the banking function, is the crime of money laundering which extensive efforts are being taken at the international level to detect it. In this regard, a mechanism that is able to detect money laundering crime is valuable. Identification techniques of money laundering can help to identify and analyze fraud and scams in an organization and also reduce the risk of doing money laundering by understanding user behavior or clients. Given the importance of the subject, in order to identify the crime of money laundering in the banking network, a mechanism should be designed based on international standards. Setting this mechanism enables the banks to predict the probability of occurrence of the crime before it happens and be useful in the crime prevention. The results of the test the validity of the model show RMSE = 0.08 and the model is suitable to detect money laundering.