The Balance between Nuance and Clarity: Decluttering Tabular Sequential Graphs to Counter Money Laundering
Authors
Salomé Esteves (Feedzai), Rita Costa (Feedzai), Louise Fallon (Mastercard), Pedro Bizarro (Feedzai)
Presentation
- Session
- Form Follows Function
- Time
- Tuesday, Nov 10, 10:45 – 10:54 (US/Eastern) · session 10:00 – 11:30
- Location
- Hall Essex north
Links
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Keywords
Graph visualization, network, node grouping
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 qualitative user study with experts suggests 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.
For Practitioners
Our work relies on the underlying structures of fraud and money-laundering analysis. Practitioners in this field can benefit from the tabular sequential graphs and node grouping methods we propose. However, these decluttering methods are replicable in other domains where this specific visualization structure is applicable. We also believe our proposal offers value to information designers by introducing a new form of graph visualization, specific node grouping techniques, and a tailored meta-node.