Identifying the Suspected Cases of Money Laundering in Banking Using Multiple Attribute Decision Making (MADM)

Abstract

Money laundering is among the most common financial crimes that negatively affect countries' economies and hurt their social and political relations. With the increasing growth of e-banking and the increase in electronic financial transactions, the identification of money laundering methods and behaviors has become more complex; because money launderers, by accessing the Internet and using new technologies, find new ways to legalize their illegal income. Although many efforts have been made to identify suspected cases of money laundering and fight against this financial crime, little success has been achieved in this regard, especially in developing countries. Hence, this study tries to identify the risk factors involved in money laundering in banking transactions. To this end, multiple attribute decision-making methods, such as the Shannon entropy method, hierarchical analysis, and two-level fuzzy hierarchical analysis, have been used to assess and score the risk of various transactions in money laundering. The results indicated that the highest risk of money laundering was in the POS transactions.