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 multidimensional 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.
