Supplier Impersonation Fraud Detection in Business-To-Business Transaction Networks Using Self-Organizing Maps

Supplier Impersonation Fraud (SIF) is a rising issue for Business to Business companies, as the use of remote and quick digital transactions has made the task of identifying fraudsters more difficult. In this paper, we propose data-driven fraud detection system whose goal is to provide an accurate estimation of transactions’ legitimacy by using the knowledge contained in the network of transactions created by the interaction of a company with its supplier. We consider the real dataset collected by SIS-ID for this work.