Money Laundering (ML) is the process of cleaning “dirty” money, thereby making the source of funds no longer identifiable. Detecting money laundering activities is a challenging task due to huge volumes of financial transactions being made in a global market on a daily basis. This paper proposes a novel approach for detecting money laundering transactions among large volumes of financial data in an efficient and accurate manner. We propose a framework that applies case reduction methods to progressively reduce the input data set to a significantly smaller size. The framework then scans the reduced data to find pairs of transactions with common attributes and behaviours that are potentially involved in ML activities. It then applies a clustering method to detect potential ML groups. We present preliminary experimental results that demonstrate the effectiveness of the proposed framework.
['Reza Soltani', 'Uyen Trang Nguyen', 'Yang Yang', 'Mohammad Reza Faghani', 'Alaa Yagoub', 'Aijun An']
A new algorithm for money laundering detection based on structural similarity