Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions

ArXiv ID: 2309.13662 “View on arXiv”

Authors: Unknown

Abstract

Money launderers exploit the weaknesses in detection systems by purposefully placing their ill-gotten money into multiple accounts, at different banks. That money is then layered and moved around among mule accounts to obscure the origin and the flow of transactions. Consequently, the money is integrated into the financial system without raising suspicion. Path finding algorithms that aim at tracking suspicious flows of money usually struggle with scale and complexity. Existing community detection techniques also fail to properly capture the time-dependent relationships. This is particularly evident when performing analytics over massive transaction graphs. We propose a framework (called FaSTMAN), adapted for domain-specific constraints, to efficiently construct a temporal graph of sequential transactions. The framework includes a weighting method, using 2nd order graph representation, to quantify the significance of the edges. This method enables us to distribute complex queries on smaller and densely connected networks of flows. Finally, based on those queries, we can effectively identify networks of suspicious flows. We extensively evaluate the scalability and the effectiveness of our framework against two state-of-the-art solutions for detecting suspicious flows of transactions. For a dataset of over 1 Billion transactions from multiple large European banks, the results show a clear superiority of our framework both in efficiency and usefulness.

Keywords: Graph Analysis, Temporal Graphs, Community Detection, Money Laundering Detection, 2nd Order Graph Representation, Fixed Income / Banking

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts like higher-order graph representations and temporal graph theory, giving it a moderately high math complexity. It also demonstrates strong empirical rigor through evaluation on a real-world dataset of over 1 billion transactions with comparative benchmarks, making it backtest-ready and data-heavy.

  flowchart TD
    A["<b>Research Goal:</b> Detect temporal money laundering flows in billion-scale transactions, overcoming limitations of path-finding & community detection"] --> B["<b>Input Data:</b><br>Over 1 Billion transactions<br>from multiple large European banks"]
    B --> C["<b>Framework Construction:</b><br>FaSTMAN Framework:<br>Build Temporal Transaction Graph"]
    C --> D["<b>Methodology:</b><br>2nd Order Graph Representation<br>Weighting Method (Significance Quantification)"]
    D --> E["<b>Computation:</b><br>Distribute queries on<br>smaller, densely connected networks"]
    E --> F["<b>Outcomes:</b><br>Identify networks of suspicious flows<br>Superior scalability & efficiency<br>vs. state-of-the-art"]