Detecting Suspicious Customers in Money Laundering Activities Using Weighted HITS Algorithm

Money laundering is the illicit practice of disguising huge sums of cash obtained through criminal activity, such as the funding of terrorist activities or drug trafficking, as coming from a legitimate source. The technique “launders” the money, which is thought to be dirty as a result of the illicit action, to make it appear clean. Placement, layering, and integration are the traditional three steps in the money laundering process. Money launderers are more likely to be discovered during the placement phase where depositing money with suspicious patterns into the established financial system might raise investigators' suspicious. This paper aims to establish and develop a comprehensive framework to detect suspicious customers using a modified version of Hyperlink Induced Topic Search (HITS) algorithm as a ranking algorithm. The proposed framework was found to be successful in terms of precision of suspicious customers' detection when evaluated on different money laundering datasets.