The Balance between Nuance and Clarity: Decluttering Tabular Sequential Graphs to Counter Money Laundering
For financial analysts investigating money laundering alerts, this work provides tailored visualization techniques to improve clarity and analysis efficiency, though the findings are incremental.
The paper addresses the challenge of visualizing complex money laundering transaction networks by proposing tabular sequential graphs with three grouping methods (amount-based, time-based, combined). A user study found that the most effective node reduction method was not always the most useful for analysis, revealing a trade-off between manual work and interpretation time.
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.