🤖 AI Summary
Financial criminals often obscure fund flows through complex transactions, rendering traditional network visualizations ineffective for anti-money laundering (AML) analysis. This work proposes a tabular temporal graph visualization approach tailored for AML, introducing this representation to the domain for the first time. We design three aggregation strategies for nodes and edges—based on transaction amount, time, and their joint consideration—to balance detail preservation with analytical efficiency. Leveraging these strategies, we implement an interactive visualization system and validate its efficacy through expert user studies. The findings reveal a trade-off between graph simplification and analytical utility: finer-grained representations, while imposing higher cognitive load, significantly enhance analysts’ interest and depth of insight.
📝 Abstract
Money laundering is not only about moving illicit funds, but about hiding the money's origin and traces to complicate detection. Financial criminals resort to many methods to avoid regulators and legal thresholds. But analysts investigating alerts, dedicated to pin mule accounts and track suspicious transactions daily, also have theirs. Network visualizations can be key in countering adversarial money laundering activities, especially if they provide a clear overview of the money flows and a seamless analysis experience, but they are often not structured for this type of task. That is why we propose a tabular sequential graph visualization tailored to money laundering analysis - following transactions (edges) from the victim account that triggered an alert through multiple accounts (nodes) and banks (rows). To reduce the number of nodes and edges, we propose three methods for grouping these tabular sequential graphs: an amount-based approach, a time-based approach, and a combined solution that considers both the transaction amount and its order. A user study with experts revealed that the most effective method in node reduction was not necessarily the most interesting for analysis and that there is a trade-off between manual work and time for interpretation in more granular graphs.