Designing a New Method for Detecting Money Laundering based on Social Network Analysis

Money laundering nowadays occurs as one of the most severe and common crimes with great potential to harm the economy. Discovering money laundering by different computer methods has always been necessary due to criminals' high tendency to launder money. This study has focused on catching a type of money laundering, which leaves a trace in the datasets where the process of money laundering has been done collaboratively. This crime can be uncovered merely by discovering the pattern of group behavior of individuals. In this research, the social networks analysis method has been employed to detect group behavior in money laundering. The data were simulated based on the real environment and by considering different states because of proper data inaccessibility. The patterns of placement, layering, and integration of money are initially explained in money laundering in this study, followed by drawing a social network of individuals' transactions. In the end, the main culprits and their collaborators will be introduced based on a combination of criteria of centrality and detecting communities. Three different types of data have been used aimed at assessing the accuracy of the proposed solution. The proposed solution has also been compared with essential solutions such as the support vector machine, decision tree, and deep learning.