Privacy-preserving data mining of cross-border financial flows

Abstract

Criminal networks continue to utilize the global financial system to launder their proceeds of crime, despite the broad enactment of anti-money laundering (aml) laws and regulations in many countries. Money laundering consumes capital resources and the tax revenue needed to fund infrastructure development and alleviate poverty in developing market economies. This paper, therefore, expands on the tools available for enabling privacy-preserving data mining in multi-dimensional datasets to combat cross-border money laundering. Most importantly, this paper develops a novel measure for detecting anomalies in cross-border financial networks, allowing financial institutions and regulatory organizations to identify suspicious nodes. The research used a sample dataset comprising international financial transactions and a hypothetical dataset to demonstrate the measure of node importance and the symmetric-key encryption algorithm. The results support the argument that the proposed network measure can detect node anomalies in the cross-border financial flows network, enabling regulatory authorities and law enforcement agencies to investigate financial transactions for suspicious activity and criminal conduct. The encryption algorithm can ensure adherence to information privacy laws and policies without compromising data reusability. Hence, the proposed methodology can improve the proactive management of money laundering risks associated with cross-border fund flows for the global financial system’s benefit.