The Balance between Nuance and Clarity: Decluttering Tabular Sequential Graphs to Counter Money Laundering

TL;DR

This paper introduces a specialized tabular sequential graph visualization for money laundering analysis, employing grouping methods to reduce complexity and improve interpretability for investigators.

Contribution

It proposes three novel grouping techniques for visualizing transaction sequences, balancing clarity and manual effort in detecting illicit activities.

Findings

Amount-based grouping reduces nodes effectively.

Time-based grouping offers a different perspective on transaction sequences.

Trade-offs exist between granularity and interpretability in graph analysis.

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.