Breaking Through Opacity: A Context-Aware Data-Driven Conceptual Design for a Predictive Anti Money Laundering System

We studied the problem of money laundering. Current anti money laundering artificial systems solve this problem by tracking transactions. However, several inherent aspects impede a full uncovering of the underlying process; most arguably the abstract nature of transactions and the `legitimacy dilemma'. We circumvent these issues by proposing a context-aware and data-driven software/hardware system that tracks physical money rather than abstract transactions. The proposed system identifies banknotes by their unique serial numbers, allowing a meta data component that accumulates further contextual information to better detect unusual patterns of transactional behaviours. In addition, using `social network analysis', the system can be used to predict future money laundering processes.