A Framework to Score the Risk Associated with Suspicious Money Laundering Activity and Social Media Profile

Abstract

Money laundering has immense entailments. The criminal who possesses black money and wants to mask it as legitimate must fabricate the source to look genuine. It makes the crime organized and more systematic to break the financial system. The existing AML (Anti Money Laundering) solutions and its design based on the creation of a transaction profile. Most of the leading AML software focuses on financial transactions and rarely focuses on linked suspicious individual’s social media profiles. Social networking is one of the most popular platforms to interact with others and millions of users use these platforms to communicate with each other from around the world. At the same time, the web has plenty of social and demographic information to create an accurate profile that aims to construct a legitimate profile. This paper consolidates the fragmented discussion from several articles and provides a detailed view of fraud profile identification.  Practical insights are identified from various AML solutions and summarized from an extensive literature review. The risk scoring framework and definitions of filters can be widened to include more parameters for effective alert generation. In this paper, we propose an approach and risk scoring framework to assess customer profiles that drive the suspicious profile or transactions based on social media attributes.